Evaluating the performance of a simple inductive procedure in the presence of overfitting error, A.B. Nobel, Proceedings of the Fourth Annual Conference on Computational Learning Theory, pp.267-274, Santa Cruz, CA, 1991.

A Recurrence theorem for dependent processes with applications to data compression, A.B. Nobel and A.D. Wyner, IEEE Transactions on Information Theory, 38:1561-1564, 1992.

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A note on uniform laws of averages for dependent processes, A.B. Nobel and A. Dembo, Statistics and Probability Letters, 17:169-172, 1993.

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A counterexample concerning uniform ergodic theorems for a class of functions, A.B. Nobel, Statistics and Probability Letters, 24:165-168, 1995.

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Termination and continuity of greedy growing for tree-structured vector quantizers, A.B. Nobel and R.A. Olshen, IEEE Transactions on Information Theory, 42:191-205, 1996.

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Consistency of data-driven histogram methods for density estimation and classification, G. Lugosi and A.B. Nobel, Annals of Statistics, 24:687-706, 1996.

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Vanishing distortion and shrinking cells, A.B. Nobel, IEEE Transactions on Information Theory, 42:1303-1305, 1996.

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Histogram regression estimation using data-dependent partitions, A.B. Nobel, Annals of Statistics, 24:1084-1105, 1996.

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Recursive partitioning to reduce distortion, A.B. Nobel, IEEE Transactions on Information Theory, 43:1122-1133, 1997.

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Density estimation from an individual numerical sequence, A.B. Nobel, G. Morvai and S. Kulkarni, IEEE Transactions on Information Theory, 44:537-541, 1998.

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On density estimation from an ergodic process, T.M. Adams and A.B. Nobel, Annals of Probability, vol. 26:794-804, 1998.

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Limits to classification and regression estimation from ergodic processes, A.B. Nobel, Annals of Statistics, 27:262-273, 1999.

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Adaptive model selection using empirical complexities, G. Lugosi and A.B. Nobel, Annals of Statistics, 27:1830-1864, 1999.

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Regression estimation from an individual stable sequence, G. Morvai, S.R. Kulkarni, and A.B. Nobel, Statistics, 33:99-118, 1999.

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First order predictive sequences and induced transformations, A.B. Nobel, Technical Report #2367, Department of Statistics, UNC Chapel Hill, 1999.

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Finitary reconstruction of a measure preserving transformation, T.M. Adams and A.B. Nobel, Israel Journal of Mathematics, 126:309-326, 2001.

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Estimating a function from ergodic samples with additive noise, A.B. Nobel and T.M. Adams, IEEE Transactions on Information Theory, 47:2895-2902, 2001.

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Consistent estimation of a dynamical map, A.B. Nobel, in the collection Nonlinear Dynamics and Statistics, pp.267-280, edited by A.I. Mees, Birkhauser, Boston, 2001.

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Analysis of a complexity based pruning method for classification trees, A.B. Nobel, IEEE Transactions on Information Theory, 48:2362-2368, 2002.

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On optimal sequential prediction schemes for general processes, A.B. Nobel, IEEE Transactions on Information Theory, vol. 49:83-98, 2003.

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Indistinguishability of absolutely continuous and singular distributions, S.P. Lalley and A.B. Nobel, Statistics and Probability Letters, 62:145-154, 2003.

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Repeated Observation of Breast Tumor Subtypes in Independent Gene Expression Data Sets, T. Sorlie, R. Tibshirani, J. Parker, T. Hastie, J.S. Marron, A. Nobel, S. Deng,H. Johnsen, R. Pesich, S. Geisler, C.M. Perou, P.E. Lonning, P.O. Brown, A-L. Borresen-Dale and D. Botstein, Proceedings of the US National Academy of Sciences, 100:8418-8423, 2003.

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Some statistical properties of memoryless individual sequences, A.B. Nobel, IEEE Transactions on Information Theory, 50:1497-1505, 2004.

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Significance analysis of functional categories in gene expression studies: a structured permutation approach, W.T. Barry, A.B. Nobel and F.A. Wright, Bioinformatics, 21:1943-1949, 2005

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ChIPOTle: A user-friendly tool for the analysis of ChIP-chip data, M.J. Buck, A.B. Nobel and J.D. Lieb, Genome Biology, 6:R97, 2005.

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Understanding Patterns of TCP Connection Usage with Statistical Clustering, F. Hernández-Campos, A.B. Nobel, F.D. Smith, K. Jeffay. Proceedings of the Thirteenth IEEE/ACM International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Atlanta, GA, September 2005.

Mining Approximate Frequent Itemsets from Noisy Data, J. Liu, S. Paulsen, W. Wang, A. Nobel and J. Prins, Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM), Houston, TX, November, 2005.

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Hypothesis testing for families of dependent processes, A.B. Nobel, Bernoulli, 12:251-269, 2006.

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Mining Approximate Frequent Itemsets in the Presence of Noise: Algorithms and Analysis, J. Liu, S. Paulsen, X. Sun, W. Wang, A.B. Nobel and J. Prins, Proceedings of the 2006 SIAM Conference on Data Mining (SDM), Bethesda, MD, April 2006.

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The Molecular Portraits of Breast Tumors Are Conserved Across Microarray Platforms, Z. Hu, C. Fan, D.S. Oh, J.S. Marron, X. He, B.F. Qaqish, C. Livasy, L.A. Carey, E. Reynolds, L. Dressler, A. Nobel, J. Parker, M.G. Ewend, L.R. Sawyer, D. Xiang, J. Wu, Y. Liu, R. Nanda, M. Tretiakova, A.R. Orrico, D. Dreher, J.P. Palazzo, L. Perreard, E. Nelson, M. Mone, H. Hansen, M. Mullins, J.F. Quackenbush, O.I. Olopade, P.S. Bernard and C.M. Perou, BMC Genomics, 7:96, 2006.

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Significance and Recovery of Block Structures in Binary Matrices with Noise, X. Sun and A.B. Nobel, Proceedings of the 19th Annual Conference on Learning Theory (COLT), H.U. Simon and G. Lugosi eds., Springer, 2006.

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Different Gene Expression-based Predictors for Breast Cancer Patients are Concordant, C. Fan, D.S. Oh, L. Wessels, B. Weigelt, D.S. Nuyten, A. Nobel, L.J. van’t Veer, and C.M. Perou, The New England Journal of Medicine, 355:560-569, 2006.

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Denoising deterministic time series, S.P. Lalley and A.B. Nobel, Dynamics of Partial Differential Equations, 3:259-279, 2006.

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Gene expression profiles do not consistently predict the clinical treatment response in locally advanced breast cancer, T. Sørlie, C.M. Perou, C. Fan, S. Geisler, T. Aas, A. Nobel, G. Anker, L.A. Akslen, D. Botstein, A-L. Børresen-Dale, and P.E. Lønning, Molecular Cancer Therapeutics 5:2914-2918, 2006.

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A statistical framework for testing functional categories in microarray data, W.T. Barry, A.B. Nobel and F.A. Wright, The Annals of Applied Statistics, vol. 2, pp.286-315, 2008.

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Online prediction algorithms for aggregation of arbitrary estimators of a conditional mean, F. Bunea and A.B. Nobel, IEEE Transactions on Information Theory, vol. 54, pp.1725-1735, 2008.

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Merging two gene expression studies via cross platform normalization, A.A. Shabalin, H. Tjelmeland, C. Fan, C.M. Perou and A.B. Nobel, Bioinformatics, vol. 24, pp.1154-1160, 2008.

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On the size and recovery of submatrices of ones in a random binary matrix, X. Sun and A.B. Nobel, Journal of Machine Learning Research, vol. 9, pp.2431-2453, 2008.

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Statistical significance of clustering for high-dimension, low-sample size data, Y. Liu, D.N. Hayes, A.B. Nobel and J.S. Marron, Journal of the American Statistical Association, vol.103 pp.1281-1293, 2008.

FastMap: Fast eQTL mapping in homozygous populations, D.M. Gatti, A.A. Shabalin, T-C. Lam, F.A. Wright, I. Rusyn and A.B. Nobel, Bioinformatics, vol. 25, pp.482-489, 2008.

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Supervised risk predictor of breast cancer based on intrinsic subtypes, J.S. Parker, M. Mullins, M.C.U. Cheang, S. Leung, D. Voduc, T. Vickery, S. Davies, C. Fauron, X. He, Z. Hu, J.F. Quackenbush, I.J. Stijleman, J. Palazzo, J.S. Marron, A.B. Nobel, E. Mardis, T.O. Nielsen, M.J. Ellis, C.M. Perou, P.S. Bernard, Journal of Clinical Oncology, JCO.2008.18.1370, 2009.

A Bayesian model for cross-study differential gene expression (with discussion), R.B. Scharpf, H. Tjelmeland, G. Parmigiani and A.B. Nobel, Journal of the American Statistical Association, vol.104, pp.1295-1310, 2009.

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Finding Large Average Submatrices in high dimensional data, A.A. Shabalin, V.J. Weigman, C.M. Perou and A.B. Nobel, Annals of Applied Statistics, vol.3, pp.985-1012, 2009.

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The Set2/Rpd3S pathway suppresses cryptic transcription without regard to gene length or transcription frequency, C.R. Lickwar, B. Rao, A.A. Shabalin, A.B. Nobel, B.D. Strahl, J.D. Lieb, PLoS ONE 4(3):e4866, 2009.

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Uniform convergence of Vapnik-Chervonenkis classes under ergodic sampling, T.M. Adams and A.B. Nobel, Annals of Probability, vol.38, pp.1345-1367, 2010.

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The gap dimension and uniform convergence under ergodic sampling, T.M. Adams and A.B. Nobel, arXiv:1007.2964, 2010.

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Heading down the wrong pathway: on the influence of correlation within gene sets, D.M. Gatti, W.T. Barry, A.B. Nobel, I. Rusyn, F.A. Wright, BMC Genomics, 11:574, 2010.

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DiNAMIC: a method to identify recurrent DNA copy number aberrations in tumors, V. Walter, A.B. Nobel, F.A. Wright, Bioinformatics 27:5, 2011.

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Discussion of “Adaptive confidence intervals for the test error in classification” (by E.B. Laber and S.A. Murphy), A.B. Nobel and S. Wei, JASA, vol.106, pp.931-936, 2011.

Discussion of “Population value decomposition, a framework for the analysis of image populations” (by C.M. Crainiceanu, B.S. Caffo, S. Luo, V. Zipunnikov and N.M. Punjabi), E.F. Lock, A.B. Nobel, and J.S. Marron, JASA, vol.106, pp.798-802, 2011.

Uniform approximation of VC classes, T.M. Adams and A.B. Nobel, Bernoulli, vol.18, pp.1310-1319, 2012.

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Reprioritizing genetic associations in hit regions using LASSO-based resample model averaging, W. Valdar, J. Sabourin, A.B. Nobel, and C.C. Holmes, Genetic Epidemiology, vol.36, pp.451-462, 2012.

Basal-like Breast cancer DNA copy number losses identify genes involved in genomic instability, response to therapy, and patient survival, V. Weigman, H.H. Chao, A.A. Shabalin, X. He, J.S. Parker, S.H. Nordgard, T. Grushko,

D. Huo, C. Nwachukwu, A.B. Nobel, V.N. Kristensen, A.L. Borresen-Dale, O.I. Olopade, C.M. Perou, Breast Cancer Research and Treatment, vol.133, pp.865-880, 2012.

On the maximal size of large-average and ANOVA-type submatrices in a Gaussian random matrix, X. Sun and A.B. Nobel, Bernoulli, vol.19, pp.275-294, 2013.

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Identification of recurrent DNA copy number aberrations in tumors, V. Walter, A.B. Nobel, D.N. Hayes, and F.A. Wright, in Statistical Diagnostics for Cancer: Analyzing High-Dimensional Data, M. Dehmer and F. Emmert-Streib editors, pp.241-260, Wiley-Blackwell, 2013.

A counterexample concerning the extension of uniform strong laws to ergodic processes, T.M. Adams and A.B. Nobel, in “A Festschrift in Honor of Jon A. Wellner”, IMS collections, vol.9, pp.1-4, 2013.

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Joint and individual variation explained (JIVE) for integrated analysis of multiple datatypes, E.F. Lock, K.A. Hoadley, J.S. Marron, and A.B. Nobel, The Annals of Applied Statistics, vol.7, pp.523-542, 2013.

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Reconstruction of a low-rank matrix in the presence of Gaussian noise, A. Shabalin and A.B. Nobel, Journal of Multivariate Analysis, vol.118, pp.67-76, 2013.

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Measuring the statistical significance of local connections in directed networks, J.D. Wilson, S. Bhamidi, and A.B. Nobel, NIPS 2013, Workshop on Frontiers of Network Analysis, December, 2013.

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A testing based extraction algorithm for identifying significant communities in networks, J.D. Wilson, S. Wang, P.J. Mucha, S. Bhamidi, and A.B. Nobel, the Annals of Applied Statistics, vol.8, pp.1853-1891, 2014.

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Consistency of maximum likelihood estimation for some dynamical systems, K. McGoff, S. Mukherjee, A.B. Nobel, and N. Pillai, the Annals of Statistics, vol.43, pp.1-29, 2015.

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Fine-mapping additive and dominant SNP effects using group-LASSO and Fractional Resample Model Averaging, J. Sabourin, A.B. Nobel, and W. Valdar, Genetic Epidemiology, vol.39, pp.77-88, 2015.

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The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans, The GTEx Consortium, Science, vol.348, pp.648-660, 2015.

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Consistent testing for recurrent genomic aberrations, V. Walter, F.A. Wright, and A.B. Nobel, Biometrika, vol.102, pp.783-796, 2015.

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A permutation approach for selecting the penalty parameter in penalized model selection, J. Sabourin, W. Valdar, and A.B. Nobel, Biometrics, vol.71, pp.1185-1194, 2015.

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Entropy and the uniform mean ergodic theorem for a family of sets, T.M. Adams and A.B. Nobel, Transactions of the American Mathematical Society, vol.369, pp.605-622, 2017.

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Energy landscape for large average submatrix detection problems in Gaussian random matrices, S. Bhamidi, P.S. Dey and A.B. Nobel. Submitted.

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Change point detection in network models: Preferential attachment and long range dependence, S. Bhamidi, J. Jin, and A.B. Nobel. Submitted.

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An empirical Bayes approach for multiple tissue eQTL analysis, G. Li, A.A. Shabalin, I. Rusyn, F.A. Wright, and A.B. Nobel. Submitted.

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The continuous configuration model: a null model for community detection on weighted networks, J. Palowitch, S. Bhamidi, and A.B. Nobel. Submitted.

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Optimal tracking and estimation for dynamical systems, K. McGoff and A.B. Nobel. Submitted.

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A testing-based approach to the discovery of differentially correlated variable sets, K. Bodwin, K. Zhang, and A.B. Nobel. Submitted.

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Estimation of interpretable eQTL effect sizes using a log of linear model, J. Palowitch, A. Shabalin, Y. Zhou, A.B. Nobel, and F.A. Wright. Submitted.

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Empirical risk minimization and complexity of dynamical models, K. McGoff and A.B. Nobel. Submitted.

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HT-eQTL: Integrative eQTL analysis in a large number of human tissues, G. Li, D.D. Jima, F.A. Wright, A.B. Nobel. Submitted.

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Community extraction in multilayer networks with heterogeneous community structure, J.D. Wilson, J. Palowitch, S. Bhamidi, A.B. Nobel. Submitted.

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Large subgraphs in pseudo-random graphs, A. Basak, S. Bhamidi, S. Chakraborty, A.B. Nobel.

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