Lawrence D. Brown

Lawrence D. Brown
  • Miers Busch Professor
  • Professor of Statistics

Research Interests: empirical queueing science, foundations of statistics, nonparametric function estimation, sampling theory (census data), statistical decision theory, statistical inference

Links: CV, Personal Website

Overview

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/.

Education

PhD, Cornell University, 1964
BS, California Institute of Technology; 1961

Awards

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)

Academic Positions Held

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

Professional Leadership

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

Personal Page

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Research

Teaching

All Courses

  • STAT1010 - Intro Business Stat

    Data summaries and descriptive statistics; introduction to a statistical computer package; Probability: distributions, expectation, variance, covariance, portfolios, central limit theorem; statistical inference of univariate data; Statistical inference for bivariate data: inference for intrinsically linear simple regression models. This course will have a business focus, but is not inappropriate for students in the college. This course may be taken concurrently with the prerequisite with instructor permission.

  • 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.

  • STAT4310 - Statistical Inference

    Graphical displays; one- and two-sample confidence intervals; one- and two-sample hypothesis tests; one- and two-way ANOVA; simple and multiple linear least-squares regression; nonlinear regression; variable selection; logistic regression; categorical data analysis; goodness-of-fit tests. A methodology course. This course does not have business applications but has significant overlap with STAT 1010 and 1020. 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

Awards and Honors

  • 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

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