Research Interests: empirical queueing science, foundations of statistics, nonparametric function estimation, sampling theory (census data), statistical decision theory, statistical inference
Links: CV, Personal Website
Professor Lawrence D. Brown passed away on February 21, 2018, at the age of 77.
More information about his life can be found here: https://imstat.org/2018/05/15/obituary-lawrence-brown-1940-2018/.
PhD, Cornell University, 1964
BS, California Institute of Technology; 1961
Member, National Academy of Sciences
DSc (honorary) Purdue University, 1993
Fellow, Institute of Mathematical Statistics and American Statistical Association
Recipient, Wilks Memorial Award (of the American Statistical Association), 2002
CR and B Rao Prize in statistics
Provost’s Award for Doctoral Education (UPenn)
Wharton: 1994-2018 (named Miers Busch, W’1885, Professor, 1994).
Previous appointments: Cornell University; Rutgers University; University of California, Berkeley.
Visiting appointments: University of California, Los Angeles; Hebrew University; Technion, Haifa, Israel; Birkbeck College, London; Peking University and Chinese National Academy of Sciences, Beijing
National Academy of Sciences, Section 32 Chairman (Applied Mathematical Sciences), 2000-2002
Member, NRC Select Committee to Review U.S. Census for 2000, 1998-2004
Member, NRC Committee on National Statistics, 1999-2005
Chairman, NRC Committee on National Statistics, 2010-2018
Member, NAS Report Review Committee
Chairman, NRC Committee to Review Research and Development Statistics program at NSF, 2002-2005
Member, NRC Panel on Coverage Evaluation in the 2010 Census, 2004-2008
Arun Kumar Kuchibhotla, Lawrence D. Brown, Andreas Buja, Edward I. George, Linda Zhao (2021), Uniform-in-Submodel Bounds for Linear Regression in a Model Free Framework, Econometric Theory, (in press) ().
Arun Kumar Kuchibhotla, Lawrence D. Brown, Andreas Buja, Edward I. George, Linda Zhao (2020), A Model Free Perspective for Linear Regression: Uniform-in-model Bounds for Post Selection Inference, Econometric Theory, (to appear) ().
Abstract: For the last two decades, high-dimensional data and methods have proliferated throughout the literature. The classical technique of linear regression, however, has not lost its touch in applications. Most high-dimensional estimation techniques can be seen as variable selection tools which lead to a smaller set of variables where classical linear regression technique applies. In this paper, we prove estimation error and linear representation bounds for the linear regression estimator uniformly over (many) subsets of variables. Based on deterministic inequalities, our results provide “good” rates when applied to both independent and dependent data. These results are useful in correctly interpreting the linear regression estimator obtained after exploring the data and also in post model-selection inference. All the results are derived under no model assumptions and are non-asymptotic in nature.
Richard A. Berk, Andreas Buja, Lawrence D. Brown, Edward I. George, Arun Kumar Kuchibhotla, Weijie Su, Linda Zhao (2020), Assumption Lean Regression, American Statistician, (in press) ().
Arun Kumar Kuchibhotla, Lawrence D. Brown, Andreas Buja, Edward I. George, Linda Zhao (2020), Valid Post-selection Inference in Model-free Linear Regression, Annals of Statistics, 48 (5), pp. 2953-2981.
Abstract: This paper provides multiple approaches to perform valid post-selection inference in an assumption-lean regression analysis. To the best of our knowledge, this is the first work that provides valid post-selection inference for regression analysis in such a general settings that include independent, m-dependent random variables.
Andreas Buja, Lawrence D. Brown, Richard A. Berk, Edward I. George, Emil Pitkin, Mikhail Traskin, Kai Zhang, Linda Zhao (2019), Models as Approximations I: Consequences Illustrated with Linear Regression, Statistical Science, 34 (4), pp. 523-544.
Andreas Buja, Lawrence D. Brown, Arun Kumar Kuchibhotla, Richard A. Berk, Edward I. George, Linda Zhao (2019), Models as Approximations II: A Model-Free Theory of Parametric Regression, Statistical Science, 34 (4), pp. 345-365.
Arun Kumar Kuchibhotla, Lawrence D. Brown, Andreas Buja, Junhui Cai (Working), All of Linear Regression.
Anru Zhang, Lawrence D. Brown, Tony Cai (2019), Semi-supervised Inference: General Theory and Estimation of Means, Annals of Statistics, 47 (5), pp. 2538-2566.
Daniel McCarthy, Kai Zhang, Lawrence D. Brown, Richard A. Berk, Andreas Buja, Edward I. George, Linda Zhao (2018), Calibrated Percentile Double Bootstrap For Robust Linear Regression Inference, Statistica Sinica, 28 (4), pp. 2565-2589.
Arun Kumar Kuchibhotla, Lawrence D. Brown, Andreas Buja (Working), Model-free Study of Ordinary Least Squares Linear Regression.
Further development of the material in STAT 1110, in particular the analysis of variance, multiple regression, non-parametric procedures and the analysis of categorical data. Data analysis via statistical packages. This course may be taken concurrently with the prerequisite with instructor permission.
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.
Dissertation