Peter Bickel's research spans a number of areas. In his work on semiparametric models (he is a co-author of the recent book Efficient and Adaptive Estimation for Semiparametric Models), he uses asymptotic theory to guide development and assessment of such models. His studies of hidden Markov models, which are important in such diverse fields as speech recognition and molecular biology, are directed toward understanding how well the method of maximum likelihood performs. He is also interested in the bootstrap, in particular in constructing diagnostic measures to detect malfunction of this technique. Recently he has become involved in developing empirical statistical models for genomic sequences. He is a co-author of the well known book Mathematical Statistics: Basic Ideas and Selected Topics.

    Working papers:

Peter Bickel, Ya'acov Ritov and Alexander Tsybakov (2008)

Hierarchical Selection of Variables in Sparse High-dimensional Regression [pdf]

Nicolai Meinshausen, Peter Bickel, and John Rice (2008)

Efficient Blind Search: Optimal Power of Detection under Computational Cost Constraints [pdf]

Chris Snyder, Thomas Bengtsson, Peter Bickel, and Jeff Anderson (2008)

Obstacles to High-dimensional Particle Filtering [pdf]

Peter Bickel, Bas Kleijn and John Rice (2008)

Event Weighted Tests for Detecting Periodicity in Photon Arrival Times [pdf], to appear, ASTROPHYSICAL JOURNAL

Peter Bickel and Ya'acov Ritov (2008)

Discussion of Mease and Wyner: And yet it overfits, [pdf], to appear, JOURNAL OF MACHINE LEARNING RESEARCH

Peter Bickel and Ya'acov Ritov (2008)

Discussion of Treelets by Lee, Nadler and Wasserman, [pdf], to appear, ANNALS OF APPLIED STATISTICS

Peter Bickel and Liza Levina (2008)

Covariance Regularization by Thresholding [pdf], to appear, ANNALS OF STATISTICS

Rothman, A.J., Bickel, P.J., Levina, E., and Zhu, J. (2008).

    Sparse Permutation Invariant Covariance Estimation. [pdf], to appear, ELECTRIC JOURNAL OF STATISTICS

Peter Bickel, Ya'acov Ritov and Alexander Tsybakov (2008)

Simultaneous Analysis of Lasso and Dantzig Selector [pdf], to appear, ANNALS OF STATISTICS

Peter Bickel, Anat Sakov (2005)

On the Choice of m in the m out of n Bootstrap and its Application to Confidence Bounds for Extreme Percentiles [pdf], to appear, STATISTICA SINICA

　

    Recent publications:

　

　

Peter Bickel, Bo Li and Thomas Bengtsson

Sharp Failure Rates for the Bootstrap Particle Filter in High Dimensions [pdf], IMS Collections: Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh, Vol.3, 318-329, 2008

Thomas Bengtsson, Peter Bickel and Bo Li

Curse of Dimensionality Revisited: the Collapse of Importance Sampling in Very Large Scale Systems [pdf], IMS Collections: Probability and Statistics: Essays in Honor of David A. Freedman, Vol.2, 316-334, 2008

Peter Bickel and Elizaveta Levina

     Regularized estimation of large covariance matrices [pdf], ANNALS OF STATISTICS, 36(1), 199-227, 2008

Peter Bickel

     Discussion: the Dantzig Selector [pdf], ANNALS OF STATISTICS, 35(6), 2352-2357, 2007

Peter Bickel

     Comments on: Nonparametric Inference with GLR Tests [pdf], TEST, 16, 445-447, 2007

Peter Bickel, Chao Chen, Jaimyoung Kwon, John Rice, Erik van Zwet and Pravin Varaiya

Measuring Traffic [pdf], STATISTICAL SCIENCE, 22 (4): 581-597, 2007 

Peter Bickel, Bas Kleijn and John Rice

On Detecting Periodicity in Astronomical Point Processes [pdf], CHALLENGES IN MODERN ASTRONOMY IV: ASP CONFERENCE SERIES Vol 371, 2007

The ENCODE Project Consortium.  

     Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project.  June 14th, 2007. Nature,

         447: 799-816.

Margulies EH et al,

     Analyses of deep mammalian sequence alignments and constraint predictions for 1% of the human genome.  June 2007. Genome Research 17:

         760-774.

Peter Bickel and Bo Li

Local Polynomial Regression on Unknown Manifolds [pdf], COMPLEX DATASETS AND INVERSE PROBLEMS: TOMOGRAPHY, NETWORKS AND BEYOND, IMS Lecture Notes-Monograph Series, Vol 54, 177-186, 2007

Peter Bickel and Bo Li

Regularization in Statistics [pdf], TEST, 15 (2): 271-344 DEC 2006 

Aiyou Chen and Peter Bickel

Efficient Independent Component Analysis [pdf], ANNALS OF STATISTICS, 34(6), 2006

Elizaveta Levina, Peter Bickel

Texture Synthesis and Non-parametric Resampling of Random Fields [pdf], ANNALS OF STATISTICS, 34 (4): 1751-1773 AUG 2006

Peter Bickel, Ya'acov Ritov and Alon Zakai

Some Theory For Generalized Boosting Algorithms [pdf], JOURNAL OF MACHINE LEARNING RESEARCH, 7: 705-732 MAY 2006

Peter Bickel, Ya'acov Ritov and Thomas Stoker

Tailor-made Tests for Goodness-of-fit to Semiparametric Hypotheses [pdf], ANNALS OF STATISTICS, 34 (2): 721-741 APR 2006

Kechris KJ, Lin JC, Bickel PJ, et al.

     Quantitative exploration of the occurence of lateral gene transfer by usingnitrogen fixation genes as a case study, PROCEEDINGS OF THE

        NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 103 (25): 9584-9589,   JUN 20 2006

Aiyou Chen and Peter Bickel

Consistent Independent Component Analysis and Prewhitening [pdf], IEEE TRANSACTIONS ON SIGNAL PROCESSING 53 (10): 3625-3632 OCT 2005

Levina, Elizaveta and Peter Bickel

Maximum likelihood estimation of intrinsic dimension [pdf]. In Advances in NIPS 17, Eds. L. K. Saul, Y. Weiss, L. Bottou (2005)

Olshen, A. B.; Cosman, P. C.; Rodrigo, A. G.; Bickel, P. J.; Olshen, R. A.

Vector quantization of amino acids: Analysis of the HIV V3 loop region [pdf]. J. Statist. Plann. Inference 130 (2005), no. 1-2, 277--298.

van Zwet, Erik W.; Kechris, Katherina J.; Bickel, Peter J.; Eisen, Michael B.

Estimating Motifs Under Order Restrictions. [pdf]. Stat. Appl. Genet. Mol. Biol. 4 (2005), Art. 1, 18 pp

Peter Bickel and Levina, Elizaveta

Some theory of Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations [pdf] Bernoulli, 10 (2004), no. 6, 989--1010.

Ge, Zhiyu; Bickel, Peter J.; Rice, John A.

     An approximate likelihood approach to nonlinear mixed effects models via spline approximation. [pdf], Comput. Statist. Data Anal. 46 (2004), no. 4, 747--776.

 

　

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