Lawrence D. Brown

• 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.

  Related
  Files

  Supplement to Valid post-selection inference
• 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.
• All Research from Lawrence D. Brown »

Past Courses

• STAT1120 - Introductory Statistics

  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.

• STAT9910 - Sem in Adv Appl of Stat

  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.

• STAT9950 - Dissertation

  Dissertation

• Member, American Academy of Arts and Sciences (AAAS), 2013
• Wilks Award of the American Statistical Association, 2002
• Doctor of Science (Honorary), Purdue University, 1993
• Lady Davis Professorship (Hebrew Univ., Jerusalem), 1988
• Wald Lecturer, Institute of Mathematical Statistics, 1985

Knowledge @ Wharton

• How Dynamic Electricity Pricing Can Improve Market Efficiency, Knowledge @ Wharton - 11/12/2024
• Is Algorithmic Management Too Controlling?, Knowledge @ Wharton - 11/12/2024
• The Hidden Cost of AI Energy Consumption, Knowledge @ Wharton - 11/12/2024
• Charitable and Political Giving | Pinar Yildirim, Knowledge @ Wharton - 11/12/2024
• The Future of Banking, Knowledge @ Wharton - 11/11/2024
• Election Statistics, Knowledge @ Wharton - 11/06/2024
• Robots Are Taking Over Low-skilled Jobs — and Changing Votes, Knowledge @ Wharton - 11/05/2024
• Crypto Politics: Liberals’ and Conservatives’ Confidence in Cryptocurrencies, Knowledge @ Wharton - 11/05/2024
• Leadership and Elections | Mike Useem, Knowledge @ Wharton - 11/05/2024
• Automation Is Harming Low-skilled Workers and Possibly Their Voting Preferences, Knowledge @ Wharton - 11/01/2024