Art Owen

All Publications

• SUPER-POLYNOMIAL ACCURACY OF MULTIDIMENSIONAL RANDOMIZED NETS USING THE MEDIAN-OF-MEANS MATHEMATICS OF COMPUTATION Pan, Z., Owen, A. B. 2024

  View details for DOI 10.1090/mcom/3880

  View details for Web of Science ID 001208341500001

• Estimating means of bounded random variables by betting JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY Owen, A. B. 2024; 86 (1)

  View details for DOI 10.1093/jrsssb/qkad116

  View details for Web of Science ID 001163100700017

• A central limit theorem for the Benjamini-Hochberg false discovery proportion under a factor model BERNOULLI Kluger, D. M., Owen, A. B. 2024; 30 (1): 743-769

  View details for DOI 10.3150/23-BEJ1615

  View details for Web of Science ID 001171838900025

• A GENERAL CHARACTERIZATION OF OPTIMAL TIE-BREAKER DESIGNS ANNALS OF STATISTICS Li, H. H., Owen, A. B. 2023; 51 (3): 1030-1057

  View details for DOI 10.1214/23-AOS2275

  View details for Web of Science ID 001055382500004

• The nonzero gain coefficients of Sobol?s sequences are always powers of two JOURNAL OF COMPLEXITY Pan, Z., Owen, A. B. 2023; 75

  View details for DOI 10.1016/j.jco.2022.101700

  View details for Web of Science ID 000925260500001

• Combining observational and experimental datasets using shrinkage estimators. Biometrics Rosenman, E. T., Basse, G., Owen, A. B., Baiocchi, M. 2023

  Abstract

  We consider the problem of combining data from observational and experimental sources to draw causal conclusions. To derive combined estimators with desirable properties, we extend results from the Stein shrinkage literature. Our contributions are threefold. First, we propose a generic procedure for deriving shrinkage estimators in this setting, making use of a generalized unbiased risk estimate. Second, we develop two new estimators, prove finite sample conditions under which they have lower risk than an estimator using only experimental data, and show that each achieves a notion of asymptotic optimality. Third, we draw connections between our approach and results in sensitivity analysis, including proposing a method for evaluating the feasibility of ourestimators. This article is protected by copyright. All rights reserved.

  View details for DOI 10.1111/biom.13827

  View details for PubMedID 36629736

• Kernel regression analysis of tie-breaker designs ELECTRONIC JOURNAL OF STATISTICS Kluger, D. M., Owen, A. B. 2023; 17 (1): 243-290

  View details for DOI 10.1214/23-EJS2102

  View details for Web of Science ID 000951095100005

• PREINTEGRATION VIA ACTIVE SUBSPACE SIAM JOURNAL ON NUMERICAL ANALYSIS Liu, S. N., Owen, A. 2023; 61 (2): 495-514

  View details for DOI 10.1137/22M1479129

  View details for Web of Science ID 000954803300004

• Deletion and Insertion Tests in Regression Models JOURNAL OF MACHINE LEARNING RESEARCH Hama, N., Mase, M., Owen, A. B. 2023; 24

  View details for Web of Science ID 001111563200001

• SUPER-POLYNOMIAL ACCURACY OF ONE DIMENSIONAL RANDOMIZED NETS USING THE MEDIAN OF MEANS MATHEMATICS OF COMPUTATION Pan, Z., Owen, A. 2022

  View details for DOI 10.1090/mcom/3791

  View details for Web of Science ID 000872951500001

• DETECTING MULTIPLE REPLICATING SIGNALS USING ADAPTIVE FILTERING PROCEDURES ANNALS OF STATISTICS Wang, J., Gui, L., Su, W. J., Sabatti, C., Owen, A. B. 2022; 50 (4): 1890-1909

  View details for DOI 10.1214/21-AOS2139

  View details for Web of Science ID 000847855400002

• Combining randomized field experiments with observational satellite data to assess the benefits of crop rotations on yields ENVIRONMENTAL RESEARCH LETTERS Kluger, D. M., Owen, A. B., Lobell, D. B. 2022; 17 (4)

  View details for DOI 10.1088/1748-9326/ac6083

  View details for Web of Science ID 000778724700001

• BACKFITTING FOR LARGE SCALE CROSSED RANDOM EFFECTS REGRESSIONS ANNALS OF STATISTICS Ghosh, S., Hastie, T., Owen, A. B. 2022; 50 (1): 560-583

  View details for DOI 10.1214/21-AOS2121

  View details for Web of Science ID 000758697800023

• Scalable logistic regression with crossed random effects ELECTRONIC JOURNAL OF STATISTICS Ghosh, S., Hastie, T., Owen, A. B. 2022; 16 (2): 4604-4635

  View details for DOI 10.1214/22-EJS2047

  View details for Web of Science ID 000953164900017

• On Dropping the First Sobol' Point Owen, A. B., Keller, A. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 71-86

  View details for DOI 10.1007/978-3-030-98319-2_4

  View details for Web of Science ID 000871749800004

• Propensity score methods for merging observational and experimental datasets. Statistics in medicine Rosenman, E. T., Owen, A. B., Baiocchi, M., Banack, H. R. 2021

  Abstract

  We consider how to merge a limited amount of data from a randomized controlled trial (RCT) into a much larger set of data from an observational data base (ODB), to estimate an average causal treatment effect. Our methods are based on stratification. The strata are defined in terms of effect moderators as well as propensity scores estimated in the ODB. Data from the RCT are placed into the strata they would have occupied, had they been in the ODB instead. We assume that treatment differences are comparable in the two data sources. Our first "spiked-in" method simply inserts the RCT data into their corresponding ODB strata. We also consider a data-driven convex combination of the ODB and RCT treatment effect estimates within each stratum. Using the delta method and simulations, we identify a bias problem with the spiked-in estimator that is ameliorated by the convex combination estimator. We apply our methods to data from the Women's Health Initiative, a study of thousands of postmenopausal women which has both observational and experimental data on hormone therapy (HT). Using half of the RCT to define a gold standard, we find that a version of the spiked-in estimator yields lower-MSE estimates of the causal impact of HT on coronary heart disease than would be achieved using either a small RCT or the observational component on its own.

  View details for DOI 10.1002/sim.9223

  View details for PubMedID 34671998

• A Strong Law of Large Numbers for Scrambled Net Integration SIAM REVIEW Owen, A. B., Rudolf, D. 2021; 63 (2): 360-372

  View details for DOI 10.1137/20M1320535

  View details for Web of Science ID 000674286700003

• Density Estimation by Randomized Quasi-Monte Carlo SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION Abdellah, A., L'Ecuyer, P., Owen, A. B., Puchhammer, F. 2021; 9 (1): 280–301

  View details for DOI 10.1137/19M1259213

  View details for Web of Science ID 000643273300010

• Quasi-Monte Carlo Quasi-Newton in Variational Bayes JOURNAL OF MACHINE LEARNING RESEARCH Liu, S., Owen, A. B. 2021; 22

  View details for Web of Science ID 000706449200001

• Designing experiments informed by observational studies JOURNAL OF CAUSAL INFERENCE Rosenman, E. R., Owen, A. B. 2021; 9 (1): 147-171

  View details for DOI 10.1515/jci-2021-0010

  View details for Web of Science ID 000677584800006

• Efficient Estimation of the ANOVA Mean Dimension, with an Application to Neural Net Classification SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION Owen, A. B., Hoyt, C. 2021; 9 (2): 708-730

  View details for DOI 10.1137/20M1350236

  View details for Web of Science ID 000674285900013

• ESTIMATION AND INFERENCE FOR VERY LARGE LINEAR MIXED EFFECTS MODELS STATISTICA SINICA Gao, K., Owen, A. B. 2020; 30 (4): 1741–71

  View details for DOI 10.5705/ss.202018.0029

  View details for Web of Science ID 000576056500004

• The Square Root Rule for Adaptive Importance Sampling ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION Owen, A. B., Zhou, Y. 2020; 30 (2)

  View details for DOI 10.1145/3350426

  View details for Web of Science ID 000583706000006

• MEAN DIMENSION OF RIDGE FUNCTIONS SIAM JOURNAL ON NUMERICAL ANALYSIS Hoyt, C. R., Owen, A. B. 2020; 58 (2): 1195–1216

  View details for DOI 10.1137/19M127149X

  View details for Web of Science ID 000546990100012

• Optimizing the tie-breaker regression discontinuity design ELECTRONIC JOURNAL OF STATISTICS Owen, A. B., Varian, H. 2020; 14 (2): 4004–27

  View details for DOI 10.1214/20-EJS1765

  View details for Web of Science ID 000587719400042

• PERMUTATION p-VALUE APPROXIMATION VIA GENERALIZED STOLARSKY INVARIANCE ANNALS OF STATISTICS He, H. Y., Basu, K., Zhao, Q., Owen, A. B. 2019; 47 (1): 583–611

  View details for DOI 10.1214/18-AOS1702

  View details for Web of Science ID 000451778700020

• Comment: Unreasonable Effectiveness of Monte Carlo STATISTICAL SCIENCE Owen, A. B. 2019; 34 (1): 29–33

  View details for DOI 10.1214/18-STS676

  View details for Web of Science ID 000464350600003

• Admissibility in Partial Conjunction Testing JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Wang, J., Owen, A. B. 2019; 114 (525): 158–68

  View details for DOI 10.1080/01621459.2017.1385465

  View details for Web of Science ID 000471325500017

• Importance sampling the union of rare events with an application to power systems analysis ELECTRONIC JOURNAL OF STATISTICS Owen, A. B., Maximov, Y., Chertkov, M. 2019; 13 (1): 231–54

  View details for DOI 10.1214/18-EJS1527

  View details for Web of Science ID 000465088200008

• EFFECTIVE DIMENSION OF SOME WEIGHTED PRE-SOBOLEV SPACES WITH DOMINATING MIXED PARTIAL DERIVATIVES SIAM JOURNAL ON NUMERICAL ANALYSIS Owen, A. 2019; 57 (2): 547–62

  View details for DOI 10.1137/17M1158975

  View details for Web of Science ID 000466423000002

• SINGLE NUGGET KRIGING STATISTICA SINICA Lee, M. R., Owen, A. B. 2018; 28 (2): 649–69

  View details for DOI 10.5705/ss.202016.0255

  View details for Web of Science ID 000450211500006

• CONFOUNDER ADJUSTMENT IN MULTIPLE HYPOTHESIS TESTING ANNALS OF STATISTICS Wang, J., Zhao, Q., Hastie, T., Owen, A. B. 2017; 45 (5): 1863–94

  View details for DOI 10.1214/16-AOS1511

  View details for Web of Science ID 000416455300002

• CONFOUNDER ADJUSTMENT IN MULTIPLE HYPOTHESIS TESTING. Annals of statistics Wang, J., Zhao, Q., Hastie, T., Owen, A. B. 2017; 45 (5): 1863-1894

  Abstract

  We consider large-scale studies in which thousands of significance tests are performed simultaneously. In some of these studies, the multiple testing procedure can be severely biased by latent confounding factors such as batch effects and unmeasured covariates that correlate with both primary variable(s) of interest (e.g., treatment variable, phenotype) and the outcome. Over the past decade, many statistical methods have been proposed to adjust for the confounders in hypothesis testing. We unify these methods in the same framework, generalize them to include multiple primary variables and multiple nuisance variables, and analyze their statistical properties. In particular, we provide theoretical guarantees for RUV-4 [Gagnon-Bartsch, Jacob and Speed (2013)] and LEAPP [Ann. Appl. Stat.6 (2012) 1664-1688], which correspond to two different identification conditions in the framework: the first requires a set of "negative controls" that are known a priori to follow the null distribution; the second requires the true nonnulls to be sparse. Two different estimators which are based on RUV-4 and LEAPP are then applied to these two scenarios. We show that if the confounding factors are strong, the resulting estimators can be asymptotically as powerful as the oracle estimator which observes the latent confounding factors. For hypothesis testing, we show the asymptotic z-tests based on the estimators can control the type I error. Numerical experiments show that the false discovery rate is also controlled by the Benjamini-Hochberg procedure when the sample size is reasonably large.

  View details for DOI 10.1214/16-AOS1511

  View details for PubMedID 31439967

  View details for PubMedCentralID PMC6706069

• Scrambled Geometric Net Integration Over General Product Spaces FOUNDATIONS OF COMPUTATIONAL MATHEMATICS Basu, K., Owen, A. B. 2017; 17 (2): 467-496

  View details for DOI 10.1007/s10208-015-9293-5

  View details for Web of Science ID 000398888500004

• On Shapley Value for Measuring Importance of Dependent Inputs SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION Owen, A. B., Prieur, C. 2017; 5 (1): 986–1002

  View details for DOI 10.1137/16M1097717

  View details for Web of Science ID 000424574600037

• Efficient moment calculations for variance components in large unbalanced crossed random effects models ELECTRONIC JOURNAL OF STATISTICS Gao, K., Owen, A. 2017; 11 (1): 1235–96

  View details for DOI 10.1214/17-EJS1236

  View details for Web of Science ID 000408006600039

• Statistically Efficient Thinning of a Markov Chain Sampler JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS Owen, A. B. 2017; 26 (3): 738–44

  View details for DOI 10.1080/10618600.2017.1336446

  View details for Web of Science ID 000410916600025

• Extensible grids: uniform sampling on a space filling curve JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY He, Z., Owen, A. B. 2016; 78 (4): 917-931

  View details for DOI 10.1111/rssb.12132

  View details for Web of Science ID 000380720300010

• A constraint on extensible quadrature rules NUMERISCHE MATHEMATIK Owen, A. B. 2016; 132 (3): 511-518

  View details for DOI 10.1007/s00211-015-0724-7

  View details for Web of Science ID 000372170200004

• Bi-Cross-Validation for Factor Analysis STATISTICAL SCIENCE Owen, A. B., Wang, J. 2016; 31 (1): 119-139

  View details for DOI 10.1214/15-STS539

  View details for Web of Science ID 000370283600012

• RANSFORMATIONS AND HARDY-KRAUSE VARIATION SIAM JOURNAL ON NUMERICAL ANALYSIS Basu, K., Owen, A. B. 2016; 54 (3): 1946-1966

  View details for DOI 10.1137/15M1052184

  View details for Web of Science ID 000385026000026

• Optimal multiple testing under a Gaussian prior on the effect sizes BIOMETRIKA Dobriban, E., Fortney, K., Kim, S. K., Owen, A. B. 2015; 102 (4): 753-766

  Abstract

  We develop a new method for large-scale frequentist multiple testing with Bayesian prior information. We find optimal [Formula: see text]-value weights that maximize the average power of the weighted Bonferroni method. Due to the nonconvexity of the optimization problem, previous methods that account for uncertain prior information are suitable for only a small number of tests. For a Gaussian prior on the effect sizes, we give an efficient algorithm that is guaranteed to find the optimal weights nearly exactly. Our method can discover new loci in genome-wide association studies and compares favourably to competitors. An open-source implementation is available.

  View details for DOI 10.1093/biomet/asv050

  View details for Web of Science ID 000366379000001

  View details for PubMedCentralID PMC4813057

• Genome-Wide Scan Informed by Age-Related Disease Identifies Loci for Exceptional Human Longevity. PLoS genetics Fortney, K., Dobriban, E., Garagnani, P., Pirazzini, C., Monti, D., Mari, D., Atzmon, G., Barzilai, N., Franceschi, C., Owen, A. B., Kim, S. K. 2015; 11 (12): e1005728

  Abstract

  We developed a new statistical framework to find genetic variants associated with extreme longevity. The method, informed GWAS (iGWAS), takes advantage of knowledge from large studies of age-related disease in order to narrow the search for SNPs associated with longevity. To gain support for our approach, we first show there is an overlap between loci involved in disease and loci associated with extreme longevity. These results indicate that several disease variants may be depleted in centenarians versus the general population. Next, we used iGWAS to harness information from 14 meta-analyses of disease and trait GWAS to identify longevity loci in two studies of long-lived humans. In a standard GWAS analysis, only one locus in these studies is significant (APOE/TOMM40) when controlling the false discovery rate (FDR) at 10%. With iGWAS, we identify eight genetic loci to associate significantly with exceptional human longevity at FDR < 10%. We followed up the eight lead SNPs in independent cohorts, and found replication evidence of four loci and suggestive evidence for one more with exceptional longevity. The loci that replicated (FDR < 5%) included APOE/TOMM40 (associated with Alzheimer's disease), CDKN2B/ANRIL (implicated in the regulation of cellular senescence), ABO (tags the O blood group), and SH2B3/ATXN2 (a signaling gene that extends lifespan in Drosophila and a gene involved in neurological disease). Our results implicate new loci in longevity and reveal a genetic overlap between longevity and age-related diseases and traits, including coronary artery disease and Alzheimer's disease. iGWAS provides a new analytical strategy for uncovering SNPs that influence extreme longevity, and can be applied more broadly to boost power in other studies of complex phenotypes.

  View details for DOI 10.1371/journal.pgen.1005728

  View details for PubMedID 26677855

  View details for PubMedCentralID PMC4683064

• Optimal multiple testing under a Gaussian prior on the effect sizes. Biometrika Dobriban, E., Fortney, K., Kim, S. K., Owen, A. B. 2015; 102 (4): 753-766

  Abstract

  We develop a new method for large-scale frequentist multiple testing with Bayesian prior information. We find optimal [Formula: see text]-value weights that maximize the average power of the weighted Bonferroni method. Due to the nonconvexity of the optimization problem, previous methods that account for uncertain prior information are suitable for only a small number of tests. For a Gaussian prior on the effect sizes, we give an efficient algorithm that is guaranteed to find the optimal weights nearly exactly. Our method can discover new loci in genome-wide association studies and compares favourably to competitors. An open-source implementation is available.

  View details for DOI 10.1093/biomet/asv050

  View details for PubMedID 27046938

  View details for PubMedCentralID PMC4813057

• Genome-Wide Scan Informed by Age-Related Disease Identifies Loci for Exceptional Human Longevity PLOS GENETICS Fortney, K., Dobriban, E., Garagnani, P., Pirazzini, C., Monti, D., Mari, D., Atzmon, G., Barzilai, N., Franceschi, C., Owen, A. B., Kim, S. K. 2015; 11 (12)

  Abstract

  We developed a new statistical framework to find genetic variants associated with extreme longevity. The method, informed GWAS (iGWAS), takes advantage of knowledge from large studies of age-related disease in order to narrow the search for SNPs associated with longevity. To gain support for our approach, we first show there is an overlap between loci involved in disease and loci associated with extreme longevity. These results indicate that several disease variants may be depleted in centenarians versus the general population. Next, we used iGWAS to harness information from 14 meta-analyses of disease and trait GWAS to identify longevity loci in two studies of long-lived humans. In a standard GWAS analysis, only one locus in these studies is significant (APOE/TOMM40) when controlling the false discovery rate (FDR) at 10%. With iGWAS, we identify eight genetic loci to associate significantly with exceptional human longevity at FDR < 10%. We followed up the eight lead SNPs in independent cohorts, and found replication evidence of four loci and suggestive evidence for one more with exceptional longevity. The loci that replicated (FDR < 5%) included APOE/TOMM40 (associated with Alzheimer's disease), CDKN2B/ANRIL (implicated in the regulation of cellular senescence), ABO (tags the O blood group), and SH2B3/ATXN2 (a signaling gene that extends lifespan in Drosophila and a gene involved in neurological disease). Our results implicate new loci in longevity and reveal a genetic overlap between longevity and age-related diseases and traits, including coronary artery disease and Alzheimer's disease. iGWAS provides a new analytical strategy for uncovering SNPs that influence extreme longevity, and can be applied more broadly to boost power in other studies of complex phenotypes.

  View details for DOI 10.1371/journal.pgen.1005728

  View details for Web of Science ID 000368518400057

  View details for PubMedCentralID PMC4683064

• Moment based gene set tests BMC BIOINFORMATICS Larson, J. L., Owen, A. B. 2015; 16

  Abstract

  Permutation-based gene set tests are standard approaches for testing relationships between collections of related genes and an outcome of interest in high throughput expression analyses. Using M random permutations, one can attain p-values as small as 1/(M+1). When many gene sets are tested, we need smaller p-values, hence larger M, to achieve significance while accounting for the number of simultaneous tests being made. As a result, the number of permutations to be done rises along with the cost per permutation. To reduce this cost, we seek parametric approximations to the permutation distributions for gene set tests.We study two gene set methods based on sums and sums of squared correlations. The statistics we study are among the best performers in the extensive simulation of 261 gene set methods by Ackermann and Strimmer in 2009. Our approach calculates exact relevant moments of these statistics and uses them to fit parametric distributions. The computational cost of our algorithm for the linear case is on the order of doing |G| permutations, where |G| is the number of genes in set G. For the quadratic statistics, the cost is on the order of |G|(2) permutations which can still be orders of magnitude faster than plain permutation sampling. We applied the permutation approximation method to three public Parkinson's Disease expression datasets and discovered enriched gene sets not previously discussed. We found that the moment-based gene set enrichment p-values closely approximate the permutation method p-values at a tiny fraction of their cost. They also gave nearly identical rankings to the gene sets being compared.We have developed a moment based approximation to linear and quadratic gene set test statistics' permutation distribution. This allows approximate testing to be done orders of magnitude faster than one could do by sampling permutations. We have implemented our method as a publicly available Bioconductor package, npGSEA (www.bioconductor.org) .

  View details for DOI 10.1186/s12859-015-0571-7

  View details for Web of Science ID 000353871900001

  View details for PubMedID 25928861

  View details for PubMedCentralID PMC4419444

• Data enriched linear regression ELECTRONIC JOURNAL OF STATISTICS Chen, A., Owen, A. B., Shi, M. 2015; 9 (1): 1078-1112

  View details for DOI 10.1214/15-EJS1027

  View details for Web of Science ID 000366268800036

• LOW DISCREPANCY CONSTRUCTIONS IN THE TRIANGLE SIAM JOURNAL ON NUMERICAL ANALYSIS Basu, K., Owen, A. B. 2015; 53 (2): 743-761

  View details for DOI 10.1137/140960463

  View details for Web of Science ID 000353844900003

• The Sign of the Logistic Regression Coefficient AMERICAN STATISTICIAN Owen, A. B., Roediger, P. A. 2014; 68 (4): 297-301

  View details for DOI 10.1080/00031305.2014.951128

  View details for Web of Science ID 000345139300009

• Higher order Sobol' indices INFORMATION AND INFERENCE-A JOURNAL OF THE IMA Owen, A. B., Dick, J., Chen, S. 2014; 3 (1): 59–81

  View details for DOI 10.1093/imaiai/iau001

  View details for Web of Science ID 000218924200003

• Sobol' Indices and Shapley Value SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION Owen, A. B. 2014; 2 (1): 245–51

  View details for DOI 10.1137/130936233

  View details for Web of Science ID 000421346900011

• Self-concordance for empirical likelihood CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE Owen, A. B. 2013; 41 (3): 387-397

  View details for DOI 10.1002/cjs.11183

  View details for Web of Science ID 000322963400001

• Better Estimation of Small Sobol' Sensitivity Indices ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION Owen, A. B. 2013; 23 (2)

  View details for DOI 10.1145/2457459.2457460

  View details for Web of Science ID 000318944000001

• Variance Components and Generalized Sobol' Indices SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION Owen, A. B. 2013; 1 (1): 19–41

  View details for DOI 10.1137/120876782

  View details for Web of Science ID 000213796800003

• Correct Ordering in the Zipf-Poisson Ensemble JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Dyer, J. S., Owen, A. B. 2012; 107 (500): 1510-1517

  View details for DOI 10.1080/01621459.2012.734177

  View details for Web of Science ID 000313394600020

• MULTIPLE HYPOTHESIS TESTING ADJUSTED FOR LATENT VARIABLES, WITH AN APPLICATION TO THE AGEMAP GENE EXPRESSION DATA ANNALS OF APPLIED STATISTICS Sun, Y., Zhang, N. R., Owen, A. B. 2012; 6 (4): 1664-1688

  View details for DOI 10.1214/12-AOAS561

  View details for Web of Science ID 000314458400014

• BOOTSTRAPPING DATA ARRAYS OF ARBITRARY ORDER ANNALS OF APPLIED STATISTICS Owen, A. B., Eckles, D. 2012; 6 (3): 895-927

  View details for DOI 10.1214/12-AOAS547

  View details for Web of Science ID 000314457400004

• A Sparse Transmission Disequilibrium Test for Haplotypes Based on Bradley-Terry Graphs HUMAN HEREDITY Ma, L., Wong, W. H., Owen, A. B. 2012; 73 (1): 52-61

  Abstract

  Linkage and association analysis based on haplotype transmission disequilibrium can be more informative than single marker analysis. Several works have been proposed in recent years to extend the transmission disequilibrium test (TDT) to haplotypes. Among them, a powerful approach called the evolutionary tree TDT (ET-TDT) incorporates information about the evolutionary relationship among haplotypes using the cladogram of the locus.In this work we extend this approach by taking into consideration the sparsity of causal mutations in the evolutionary history. We first introduce the notion of a Bradley-Terry (BT) graph representation of a haplotype locus. The most important property of the BT graph is that sparsity of the edge set of the graph corresponds to small number of causal mutations in the evolution of the haplotypes. We then propose a method to test the null hypothesis of no linkage and association against sparse alternatives under which a small number of edges on the BT graph have non-nil effects.We compare the performance of our approach to that of the ET-TDT through a power study, and show that incorporating sparsity of causal mutations can significantly improve the power of a haplotype-based TDT.

  View details for DOI 10.1159/000335937

  View details for Web of Science ID 000302111100008

  View details for PubMedID 22398955

  View details for PubMedCentralID PMC3357149

• Moment-Based Estimation of Stochastic Kronecker Graph Parameters INTERNET MATHEMATICS Gleich, D. F., Owen, A. B. 2012; 8 (3): 232–56

  View details for DOI 10.1080/15427951.2012.680824

  View details for Web of Science ID 000217675800002

• Outlier Detection Using Nonconvex Penalized Regression JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION She, Y., Owen, A. B. 2011; 106 (494): 626-639

  View details for DOI 10.1198/jasa.2011.tm10390

  View details for Web of Science ID 000293113300022

• CONSISTENCY OF MARKOV CHAIN QUASI-MONTE CARLO ON CONTINUOUS STATE SPACES ANNALS OF STATISTICS Chen, S., Dick, J., Owen, A. B. 2011; 39 (2): 673-701

  View details for DOI 10.1214/10-AOS831

  View details for Web of Science ID 000291183300001

• Visualizing bivariate long-tailed data ELECTRONIC JOURNAL OF STATISTICS Dyer, J. S., Owen, A. B. 2011; 5: 642-668

  View details for DOI 10.1214/11-EJS622

  View details for Web of Science ID 000293080600001

• EMPIRICAL STATIONARY CORRELATIONS FOR SEMI-SUPERVISED LEARNING ON GRAPHS ANNALS OF APPLIED STATISTICS Xu, Y., Dyer, J. S., Owen, A. B. 2010; 4 (2): 589-614

  View details for DOI 10.1214/09-AOAS293

  View details for Web of Science ID 000283528500004

• A Rotation Test to Verify Latent Structure JOURNAL OF MACHINE LEARNING RESEARCH Perry, P. O., Owen, A. B. 2010; 11: 603-624

  View details for Web of Science ID 000277186500006

• KARL PEARSON'S META-ANALYSIS REVISITED ANNALS OF STATISTICS Owen, A. B. 2009; 37 (6B): 3867-3892

  View details for DOI 10.1214/09-AOS697

  View details for Web of Science ID 000271673700006

• Aging Mice Show a Decreasing Correlation of Gene Expression within Genetic Modules PLOS GENETICS Southworth, L. K., Owen, A. B., Kim, S. K. 2009; 5 (12)

  Abstract

  In this work we present a method for the differential analysis of gene co-expression networks and apply this method to look for large-scale transcriptional changes in aging. We derived synonymous gene co-expression networks from AGEMAP expression data for 16-month-old and 24-month-old mice. We identified a number of functional gene groups that change co-expression with age. Among these changing groups we found a trend towards declining correlation with age. In particular, we identified a modular (as opposed to uniform) decline in general correlation with age. We identified potential transcriptional mechanisms that may aid in modular correlation decline. We found that computationally identified targets of the NF-KappaB transcription factor decrease expression correlation with age. Finally, we found that genes that are prone to declining co-expression tend to be co-located on the chromosome. Our results conclude that there is a modular decline in co-expression with age in mice. They also indicate that factors relating to both chromosome domains and specific transcription factors may contribute to the decline.

  View details for DOI 10.1371/journal.pgen.1000776

  View details for Web of Science ID 000273469700026

  View details for PubMedID 20019809

  View details for PubMedCentralID PMC2788246

• BI-CROSS-VALIDATION OF THE SVD AND THE NONNEGATIVE MATRIX FACTORIZATION ANNALS OF APPLIED STATISTICS Owen, A. B., Perry, P. O. 2009; 3 (2): 564-594

  View details for DOI 10.1214/08-AOAS227

  View details for Web of Science ID 000271979600004

• Properties of Balanced Permutations JOURNAL OF COMPUTATIONAL BIOLOGY Southworth, L. K., Kim, S. K., Owen, A. B. 2009; 16 (4): 625-638

  Abstract

  This paper takes a close look at balanced permutations, a recently developed sample reuse method with applications in bioinformatics. It turns out that balanced permutation reference distributions do not have the correct null behavior, which can be traced to their lack of a group structure. We find that they can give p-values that are too permissive to varying degrees. In particular the observed test statistic can be larger than that of all B balanced permutations of a data set with a probability much higher than 1/(B + 1), even under the null hypothesis.

  View details for DOI 10.1089/cmb.2008.0144

  View details for Web of Science ID 000265551400007

  View details for PubMedID 19361331

  View details for PubMedCentralID PMC3148117

• Recycling physical random numbers ELECTRONIC JOURNAL OF STATISTICS Owen, A. B. 2009; 3: 1531-1541

  View details for DOI 10.1214/09-EJS541

  View details for Web of Science ID 000207855300028

• Calibration of the empirical likelihood method for a vector mean ELECTRONIC JOURNAL OF STATISTICS Emerson, S. C., Owen, A. B. 2009; 3: 1161-1192

  View details for DOI 10.1214/09-EJS518

  View details for Web of Science ID 000207855300016

• Monte Carlo and Quasi-Monte Carlo for Statistics 8th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (MCQMC 08) Owen, A. B. SPRINGER-VERLAG BERLIN. 2009: 3–18

  View details for DOI 10.1007/978-3-642-04107-5_1

  View details for Web of Science ID 000282063700001

• LOCAL ANTITHETIC SAMPLING WITH SCRAMBLED NETS ANNALS OF STATISTICS Owen, A. B. 2008; 36 (5): 2319-2343

  View details for DOI 10.1214/07-AOS548

  View details for Web of Science ID 000260554100012

• Construction of weakly CUD sequences for MCMC sampling ELECTRONIC JOURNAL OF STATISTICS Tribble, S. D., Owen, A. B. 2008; 2: 634-660

  View details for DOI 10.1214/07-EJS162

  View details for Web of Science ID 000207854400026

• THE PIGEONHOLE BOOTSTRAP ANNALS OF APPLIED STATISTICS Owen, A. B. 2007; 1 (2): 386-411

  View details for DOI 10.1214/07-AOAS122

  View details for Web of Science ID 000261057600007

• AGEMAP: A gene expression database for aging in mice PLOS GENETICS Zahn, J. M., Poosala, S., Owen, A. B., Ingram, D. K., Lustig, A., Carter, A., Weeraratna, A. T., Taub, D. D., Gorospe, M., Mazan-Mamczarz, K., Lakatta, E. G., Boheler, K. R., Xu, X., Mattson, M. P., Falco, G., Ko, M. S., Schlessinger, D., Firman, J., Kummerfeld, S. K., Ill, W. H., Zonderman, A. B., Kim, S. K., Becker, K. G. 2007; 3 (11): 2326-2337

  Abstract

  We present the AGEMAP (Atlas of Gene Expression in Mouse Aging Project) gene expression database, which is a resource that catalogs changes in gene expression as a function of age in mice. The AGEMAP database includes expression changes for 8,932 genes in 16 tissues as a function of age. We found great heterogeneity in the amount of transcriptional changes with age in different tissues. Some tissues displayed large transcriptional differences in old mice, suggesting that these tissues may contribute strongly to organismal decline. Other tissues showed few or no changes in expression with age, indicating strong levels of homeostasis throughout life. Based on the pattern of age-related transcriptional changes, we found that tissues could be classified into one of three aging processes: (1) a pattern common to neural tissues, (2) a pattern for vascular tissues, and (3) a pattern for steroid-responsive tissues. We observed that different tissues age in a coordinated fashion in individual mice, such that certain mice exhibit rapid aging, whereas others exhibit slow aging for multiple tissues. Finally, we compared the transcriptional profiles for aging in mice to those from humans, flies, and worms. We found that genes involved in the electron transport chain show common age regulation in all four species, indicating that these genes may be exceptionally good markers of aging. However, we saw no overall correlation of age regulation between mice and humans, suggesting that aging processes in mice and humans may be fundamentally different.

  View details for DOI 10.1371/journal.pgen.0030201

  View details for Web of Science ID 000251310200024

  View details for PubMedID 18081424

  View details for PubMedCentralID PMC2098796

• Infinitely imbalanced logistic regression JOURNAL OF MACHINE LEARNING RESEARCH Owen, A. B. 2007; 8: 761-773

  View details for Web of Science ID 000247002800002

• A robust hybrid of lasso and ridge regression AMS/IMS/SIAM Joint Summer Research Conference on Machine and Statistical Learning - Prediction and Discovery Owen, A. B. AMER MATHEMATICAL SOC. 2007: 59–71

  View details for Web of Science ID 000250954400006

• Halton sequences avoid the origin SIAM REVIEW Owen, A. B. 2006; 48 (3): 487-503

  View details for DOI 10.1137/S0036144504441573

  View details for Web of Science ID 000239945600002

• Transcriptional profiling of aging in human muscle reveals a common aging signature PLOS GENETICS Zahn, J. M., Sonu, R., Vogel, H., Crane, E., Mazan-Mamczarz, K., Rabkin, R., Davis, R. W., Becker, K. G., Owen, A. B., Kim, S. K. 2006; 2 (7): 1058-1069

  Abstract

  We analyzed expression of 81 normal muscle samples from humans of varying ages, and have identified a molecular profile for aging consisting of 250 age-regulated genes. This molecular profile correlates not only with chronological age but also with a measure of physiological age. We compared the transcriptional profile of muscle aging to previous transcriptional profiles of aging in the kidney and the brain, and found a common signature for aging in these diverse human tissues. The common aging signature consists of six genetic pathways; four pathways increase expression with age (genes in the extracellular matrix, genes involved in cell growth, genes encoding factors involved in complement activation, and genes encoding components of the cytosolic ribosome), while two pathways decrease expression with age (genes involved in chloride transport and genes encoding subunits of the mitochondrial electron transport chain). We also compared transcriptional profiles of aging in humans to those of the mouse and fly, and found that the electron transport chain pathway decreases expression with age in all three organisms, suggesting that this may be a public marker for aging across species.

  View details for DOI 10.1371/journal.pgen.0020115

  View details for PubMedID 16789832

• Estimating mean dimensionality of analysis of variance decompositions JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Liu, R., Owen, A. B. 2006; 101 (474): 712-721

  View details for DOI 10.1198/016214505000001410

  View details for Web of Science ID 000238033200025

• On the Warnock-Halton quasi-standard error MONTE CARLO METHODS AND APPLICATIONS Owen, A. B. 2006; 12 (1): 47–54

  View details for DOI 10.1163/156939606776886652

  View details for Web of Science ID 000416663500003

• Quasi-Monte Carlo for integrands with point singularities at unknown locations 6th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing Owen, A. B. SPRINGER-VERLAG BERLIN. 2006: 403–417

  View details for Web of Science ID 000235235900024

• A quasi-Monte Carlo Metropolis algorithm PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Owen, A. B., Tribble, S. D. 2005; 102 (25): 8844-8849

  Abstract

  This work presents a version of the Metropolis-Hastings algorithm using quasi-Monte Carlo inputs. We prove that the method yields consistent estimates in some problems with finite state spaces and completely uniformly distributed inputs. In some numerical examples, the proposed method is much more accurate than ordinary Metropolis-Hastings sampling.

  View details for DOI 10.1073/pnas.0409596102

  View details for Web of Science ID 000230049500012

  View details for PubMedID 15956207

  View details for PubMedCentralID PMC1150275

• Control variates for quasi-Monte Carlo STATISTICAL SCIENCE Hickernell, F. J., Lemieux, C., Owen, A. B. 2005; 20 (1): 1-18

  View details for DOI 10.1214/088342304000000468

  View details for Web of Science ID 000229906000001

• Variance of the number of false discoveries JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY Owen, A. B. 2005; 67: 411-426

  View details for Web of Science ID 000229902600006

• A transcriptional profile of aging in the human kidney PLOS BIOLOGY Rodwell, G. E., Sonu, R., Zahn, J. M., Lund, J., Wilhelmy, J., Wang, L. L., Xiao, W. Z., Mindrinos, M., Crane, E., Segal, E., Myers, B. D., Brooks, J. D., Davis, R. W., Higgins, J., Owen, A. B., Kim, S. K. 2004; 2 (12): 2191-2201

  Abstract

  In this study, we found 985 genes that change expression in the cortex and the medulla of the kidney with age. Some of the genes whose transcripts increase in abundance with age are known to be specifically expressed in immune cells, suggesting that immune surveillance or inflammation increases with age. The age-regulated genes show a similar aging profile in the cortex and the medulla, suggesting a common underlying mechanism for aging. Expression profiles of these age-regulated genes mark not only age, but also the relative health and physiology of the kidney in older individuals. Finally, the set of aging-regulated kidney genes suggests specific mechanisms and pathways that may play a role in kidney degeneration with age.

  View details for DOI 10.1371/journal.pbio.0020427

  View details for PubMedID 15562319

• The host response to smallpox: Analysis of the gene expression program in peripheral blood cells in a nonhuman primate model PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Rubins, K. H., Hensley, L. E., JAHRLING, P. B., Whitney, A. R., Geisbert, T. W., HUGGINS, J. W., Owen, A., LEDUC, J. W., Brown, P. O., Relman, D. A. 2004; 101 (42): 15190-15195

  Abstract

  Smallpox has played an unparalleled role in human history and remains a significant potential threat to public health. Despite the historical significance of this disease, we know little about the underlying pathophysiology or the virulence mechanisms of the causative agent, variola virus. To improve our understanding of variola pathogenesis and variola-host interactions, we examined the molecular and cellular features of hemorrhagic smallpox in cynomolgus macaques. We used cDNA microarrays to analyze host gene expression patterns in sequential blood samples from each of 22 infected animals. Variola infection elicited striking and temporally coordinated patterns of gene expression in peripheral blood. Of particular interest were features that appear to represent an IFN response, cell proliferation, immunoglobulin gene expression, viral dose-dependent gene expression patterns, and viral modulation of the host immune response. The virtual absence of a tumor necrosis factor alpha/NF-kappaB-activated transcriptional program in the face of an overwhelming systemic infection suggests that variola gene products may ablate this response. These results provide a detailed picture of the host transcriptional response during smallpox infection, and may help guide the development of diagnostic, therapeutic, and prophylactic strategies.

  View details for DOI 10.1073/pnas.0405759101

  View details for Web of Science ID 000224688700039

  View details for PubMedID 15477590

  View details for PubMedCentralID PMC523453

• Genomic research and human subject privacy SCIENCE Lin, Z., Owen, A. B., Altman, R. B. 2004; 305 (5681): 183-183

  View details for Web of Science ID 000222501000030

  View details for PubMedID 15247459

• Nomogram for predicting the likelihood of delayed graft function in adult cadaveric renal transplant recipients JOURNAL OF THE AMERICAN SOCIETY OF NEPHROLOGY Irish, W. D., Mccollum, D. A., Tesi, R. J., Owen, A. B., Brennan, D. C., Bailly, J. E., Schnitzler, M. A. 2003; 14 (11): 2967–74

  Abstract

  Delayed graft function (DGF) is the need for dialysis in the first week after transplantation. Studied were risk factors for DGF in adult (age >/=16 yr) cadaveric renal transplant recipients by means of a multivariable modeling procedure. Only donor and recipient factors known before transplantation were chosen so that the probabilities of DGF could be calculated before transplantation and appropriate preventative measures taken. Data on 19,706 recipients of cadaveric allografts were obtained from the United States Renal Data System registry (1995 to 1998). Graft losses within the first 24 h after surgery were excluded from the analysis (n = 89). Patients whose DGF information was missing or unknown (n = 2820) and patients missing one or more candidate predictors (n = 2951) were also excluded. By means of a multivariable logistic regression analysis, factors contributing to DGF in the remaining 13,846 patients were identified. After validating the logistic regression model, a nomogram was developed as a tool for identifying patients at risk for DGF. The incidence of DGF was 23.7%. Sixteen independent donor or recipient risk factors were found to predict DGF. A nomogram quantifying the relative contribution of each risk factor was created. This index can be used to calculate the risk of DGF for an individual by adding the points associated with each risk factor. The nomogram provides a useful tool for developing a pretransplantation index of the likelihood of DGF occurrence. With this index in hand, better informed treatment and allocation decisions can be made.

  View details for DOI 10.1097/01.ASN.0000093254.31868.85

  View details for Web of Science ID 000186073500032

  View details for PubMedID 14569108

• A gene recommender algorithm to identify coexpressed genes in C-elegans GENOME RESEARCH Owen, A. B., Stuart, J., Mach, K., Villeneuve, A. M., Kim, S. 2003; 13 (8): 1828-1837

  Abstract

  One of the most important uses of whole-genome expression data is for the discovery of new genes with similar function to a given list of genes (the query) already known to have closely related function. We have developed an algorithm, called the gene recommender, that ranks genes according to how strongly they correlate with a set of query genes in those experiments for which the query genes are most strongly coregulated. We used the gene recommender to find other genes coexpressed with several sets of query genes, including genes known to function in the retinoblastoma complex. Genetic experiments confirmed that one gene (JC8.6) identified by the gene recommender acts with lin-35 Rb to regulate vulval cell fates, and that another gene (wrm-1) acts antagonistically. We find that the gene recommender returns lists of genes with better precision, for fixed levels of recall, than lists generated using the C. elegans expression topomap.

  View details for DOI 10.1101/gr.1125403

  View details for Web of Science ID 000184530900005

  View details for PubMedID 12902378

  View details for PubMedCentralID PMC403774

• A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae) PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Troyanskaya, O. G., Dolinski, K., Owen, A. B., Altman, R. B., Botstein, D. 2003; 100 (14): 8348-8353

  Abstract

  Genomic sequencing is no longer a novelty, but gene function annotation remains a key challenge in modern biology. A variety of functional genomics experimental techniques are available, from classic methods such as affinity precipitation to advanced high-throughput techniques such as gene expression microarrays. In the future, more disparate methods will be developed, further increasing the need for integrated computational analysis of data generated by these studies. We address this problem with MAGIC (Multisource Association of Genes by Integration of Clusters), a general framework that uses formal Bayesian reasoning to integrate heterogeneous types of high-throughput biological data (such as large-scale two-hybrid screens and multiple microarray analyses) for accurate gene function prediction. The system formally incorporates expert knowledge about relative accuracies of data sources to combine them within a normative framework. MAGIC provides a belief level with its output that allows the user to vary the stringency of predictions. We applied MAGIC to Saccharomyces cerevisiae genetic and physical interactions, microarray, and transcription factor binding sites data and assessed the biological relevance of gene groupings using Gene Ontology annotations produced by the Saccharomyces Genome Database. We found that by creating functional groupings based on heterogeneous data types, MAGIC improved accuracy of the groupings compared with microarray analysis alone. We describe several of the biological gene groupings identified.

  View details for DOI 10.1073/pnas.0832373100

  View details for Web of Science ID 000184222500057

  View details for PubMedID 12826619

  View details for PubMedCentralID PMC166232

• Monthly Strontium/Calcium oscillations in symbiotic coral aragonite: Biological effects limiting the precision of the paleotemperature proxy GEOPHYSICAL RESEARCH LETTERS Meibom, A., Stage, M., Wooden, J., Constantz, B. R., Dunbar, R. B., Owen, A., Grumet, N., Bacon, C. R., CHAMBERLAIN, C. P. 2003; 30 (7)

  View details for DOI 10.1029/2002GL016864

  View details for Web of Science ID 000182592200002

• Quasi-regression with shrinkage 3rd IMACS Seminar on Monte Carlo Methods (MCM 2001) Jiang, T., Owen, A. B. ELSEVIER SCIENCE BV. 2003: 231–41

  View details for Web of Science ID 000181605300003

• Data squashing by empirical likelihood DATA MINING AND KNOWLEDGE DISCOVERY Owen, A. 2003; 7 (1): 101-113

  View details for Web of Science ID 000179705200005

• The dimension distribution and quadrature test functions STATISTICA SINICA Owen, A. B. 2003; 13 (1): 1-17

  View details for Web of Science ID 000181313600001

• Plaid models for gene expression data STATISTICA SINICA Lazzeroni, L., Owen, A. 2002; 12 (1): 61-86

  View details for Web of Science ID 000174372800005

• Quasi-regression Workshop on the Complexity of Multivariate Problems An, J., Owen, A. ACADEMIC PRESS INC ELSEVIER SCIENCE. 2001: 588–607

  View details for DOI 10.1006/jcom.2001.0588

  View details for Web of Science ID 000173453400002

• Safe and effective importance sampling JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Owen, A., Zhou, Y. 2000; 95 (449): 135-143

  View details for Web of Science ID 000087845100017

• Assessing linearity in high dimensions ANNALS OF STATISTICS Owen, A. B. 2000; 28 (1): 1-19

  View details for Web of Science ID 000088077700001

• Advances in importance sampling Computational Finance 1999 Conference Owen, A., Zhou, Y. M I T PRESS. 2000: 53–65

  View details for Web of Science ID 000088555700004

• Monte Carlo, quasi-Monte Carlo, and randomized quasi-Monte Carlo 3rd International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (MCQM 98) Owen, A. B. SPRINGER-VERLAG BERLIN. 2000: 86–97

  View details for Web of Science ID 000089259100005

• Scrambling Sobol' and Niederreiter-Xing points JOURNAL OF COMPLEXITY Owen, A. B. 1998; 14 (4): 466-489

  View details for Web of Science ID 000078083600003

• Monte Carlo extension of quasi-Monte Carlo 1998 Winter Simulation Conference on Simulation in the 21st-Century (WSC 98) Owen, A. B. IEEE. 1998: 571–577

  View details for Web of Science ID 000078340100077

• Scrambled net variance for integrals of smooth functions ANNALS OF STATISTICS Owen, A. B. 1997; 25 (4): 1541-1562

  View details for Web of Science ID 000079134900009

• NONPARAMETRIC LIKELIHOOD CONFIDENCE BANDS FOR A DISTRIBUTION FUNCTION JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Owen, A. B. 1995; 90 (430): 516-521

  View details for Web of Science ID A1995RA10400016

• CONTROLLING CORRELATIONS IN LATIN HYPERCUBE SAMPLES JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Owen, A. B. 1994; 89 (428): 1517-1522

  View details for Web of Science ID A1994PU33000037

• ASYMPTOTICALLY OPTIMAL BALLOON DENSITY ESTIMATES JOURNAL OF MULTIVARIATE ANALYSIS Hall, P., Huber, C., Owen, A., Coventry, A. 1994; 51 (2): 352-371

  View details for Web of Science ID A1994PU37200009

• LATTICE SAMPLING REVISITED - MONTE-CARLO VARIANCE OF MEANS OVER RANDOMIZED ORTHOGONAL ARRAYS ANNALS OF STATISTICS Owen, A. 1994; 22 (2): 930-945

  View details for Web of Science ID A1994PN35700020

• NEURAL NETWORKS AND RELATED METHODS FOR CLASSIFICATION - DISCUSSION JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY Whittle, P., Kay, J., Hand, D. J., Tarassenko, L., Brown, P. J., Titterington, D. M., TAYLOR, C., Gilks, W. R., Critchley, F., Mayne, A. J., Wahba, G., Luttrell, S. P., Baczkowski, A. J., Mardia, K. V., Breiman, L., Buntine, W., Chatfield, C., DeVeaux, R. D., DARKEN, C. J., Ungar, L. H., Glendinning, R. H., Hastie, T., Tibshirani, R., McLachlan, G. J., Michie, D., Owen, A. B., Wolpert, D. H., Ripley, B. D. 1994; 56 (3): 437-456

  View details for Web of Science ID A1994NK40900002

• OVERFITTING IN NEURAL NETWORKS 26th Symposium on the Interface of Computing Science and Statistics - Computationally Intensive Statistical Methods Owen, A. B. INTERFACE FOUNDATION NORTH AMERICA. 1994: 57–62

  View details for Web of Science ID A1994BD22V00009

• ORTHOGONAL ARRAYS FOR COMPUTER EXPERIMENTS, INTEGRATION AND VISUALIZATION STATISTICA SINICA Owen, A. B. 1992; 2 (2): 439-452

  View details for Web of Science ID A1992JQ01200007

• A CENTRAL-LIMIT-THEOREM FOR LATIN HYPERCUBE SAMPLING JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL Owen, A. B. 1992; 54 (2): 541-551

  View details for Web of Science ID A1992HL62000012

• EMPIRICAL LIKELIHOOD FOR LINEAR-MODELS ANNALS OF STATISTICS Owen, A. 1991; 19 (4): 1725-1747

  View details for Web of Science ID A1991GZ66100002

• MULTIVARIATE ADAPTIVE REGRESSION SPLINES - DISCUSSION ANNALS OF STATISTICS Owen, A. 1991; 19 (1): 102-112

  View details for Web of Science ID A1991FF04700007

• EMPIRICAL LIKELIHOOD RATIO CONFIDENCE-REGIONS ANNALS OF STATISTICS Owen, A. 1990; 18 (1): 90-120

  View details for Web of Science ID A1990DA37500004

• USING SIMULATORS TO MODEL TRANSMITTED VARIABILITY IN IC MANUFACTURING IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING Sharifzadeh, S., Koehler, J. R., Owen, A. B., SHOTT, J. D. 1989; 2 (3): 82-93

  View details for Web of Science ID A1989AD91600003

• EMPIRICAL LIKELIHOOD RATIO CONFIDENCE-INTERVALS FOR A SINGLE FUNCTIONAL BIOMETRIKA Owen, A. B. 1988; 75 (2): 237-249

  View details for Web of Science ID A1988N941300007

• SMOOTHING WITH SPLIT LINEAR FITS TECHNOMETRICS McDonald, J. A., Owen, A. B. 1986; 28 (3): 195-208

  View details for Web of Science ID A1986D449800002

• STATISTICS, IMAGES, AND PATTERN-RECOGNITION - DISCUSSION CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE Hinkley, D. V., Morris, C. N., Moore, M., Owen, A., Chellappa, R. 1986; 14 (2): 102-111

  View details for Web of Science ID A1986D245700002