Lawrence D. Brown

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

Contact Information

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    the Wharton Department 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

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

Past Courses

  • STAT101 - Introductory Business Statistics

    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.

  • STAT102 - Introductory Business Statistics

    Continuation of STAT 101. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications.

  • STAT112 - Introductory Statistics

    Further development of the material in STAT 111, in particular the analysis of variance, multiple regression, non-parametric procedures and the analysis of categorical data. Data analysis via statistical packages.

  • STAT430 - Probability

    Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory.

  • STAT431 - 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 101 and 102.

  • STAT510 - Probability

    Elements of matrix algebra. Discrete and continuous random variables and their distributions. Moments and moment generating functions. Joint distributions. Functions and transformations of random variables. Law of large numbers and the central limit theorem. Point estimation: sufficiency, maximum likelihood, minimum variance. Confidence intervals.

  • STAT511 - 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.

  • STAT512 - Mathematical Statistics

    An introduction to the mathematical theory of statistics. Estimation, with a focus on properties of sufficient statistics and maximum likelihood estimators. Hypothesis testing, with a focus on likelihood ratio tests and the consequent development of "t" tests and hypothesis tests in regression and ANOVA. Nonparametric procedures.

  • STAT899 - Independent Study

  • STAT991 - Seminar in Advanced Application of Statistics

    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.

  • STAT995 - Dissertation

  • STAT999 - Independent Study

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

In the News

Knowledge @ Wharton

Activity

In the News

Telephone Call Centers: The Factory Floors of the 21st Century

For most of the past century, factories offered a path upward for Americans short on education. But millions of “good” manufacturing jobs have fallen victim to automation and global competition, leaving many low and semi-skilled workers to turn to a 21st century replacement: the telephone call center. What are the advantages of call centers, how can the technology best be used and what is the outlook for call-center employment in the next decade?

Knowledge @ Wharton - 2002/04/10
All News

Awards and Honors

Member, American Academy of Arts and Sciences (AAAS) 2013
All Awards