Dean P. Foster

Dean P. Foster
  • Marie and Joseph Melone Professor Emeritus of Statistics

Contact Information

  • office Address:

Research Interests: machine learning, statistical nlp., variable selection

Links: Personal Website

Overview

Education

PhD, University of Maryland, 1988
MSc, Rutgers University, 1984
MA, University of Maryland, 1982
BSc, University of Maryland, 1980

Academic Positions Held

Wharton: 1992-present (named William H. Lawrence Professor, 2007).
Previous appointment: University of Chicago.

For more information, go to My Personal Page

Continue Reading

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.

  • STAT399 - Independent Study

  • 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.

  • STAT433 - Stochastic Processes

    An introduction to Stochastic Processes. The primary focus is on Markov Chains, Martingales and Gaussian Processes. We will discuss many interesting applications from physics to economics. Topics may include: simulations of path functions, game theory and linear programming, stochastic optimization, Brownian Motion and Black-Scholes.

  • STAT471 - Modern Data Mining

    Modern Data Mining: Statistics or Data Science has been evolving rapidly to keep up with the modern world. While classical multiple regression and logistic regression technique continue to be the major tools we go beyond to include methods built on top of linear models such as LASSO and Ridge regression. Contemporary methods such as KNN (K nearest neighbor), Random Forest, Support Vector Machines, Principal Component Analyses (PCA), the bootstrap and others are also covered. Text mining especially through PCA is another topic of the course. While learning all the techniques, we keep in mind that our goal is to tackle real problems. Not only do we go through a large collection of interesting, challenging real-life data sets but we also learn how to use the free, powerful software "R" in connection with each of the methods exposed in the class.

  • 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.

  • STAT621 - Accelerated Regression Analysis for Business

    STAT 621 is intended for students with recent, practical knowledge of the use of regression analysis in the context of business applications. This course covers the material of STAT 613, but omits the foundations to focus on regression modeling. The course reviews statistical hypothesis testing and confidence intervals for the sake of standardizing terminology and introducing software, and then moves into regression modeling. The pace presumes recent exposure to both the theory and practice of regression and will not be accommodating to students who have not seen or used these methods previously. The interpretation of regression models within the context of applications will be stressed, presuming knowledge of the underlying assumptions and derivations. The scope of regression modeling that is covered includes multiple regression analysis with categorical effects, regression diagnostic procedures, interactions, and time series structure. The presentation of the course relies on computer software that will be introduced in the initial lectures.

  • STAT701 - Modern Data Mining

    Modern Data Mining: Statistics or Data Science has been evolving rapidly to keep up with the modern world. While classical multiple regression and logistic regression technique continue to be the major tools we go beyond to include methods built on top of linear models such as LASSO and Ridge regression. Contemporary methods such as KNN (K nearest neighbor), Random Forest, Support Vector Machines, Principal Component Analyses (PCA), the bootstrap and others are also covered. Text mining especially through PCA is another topic of the course. While learning all the techniques, we keep in mind that our goal is to tackle real problems. Not only do we go through a large collection of interesting, challenging real-life data sets but we also learn how to use the free, powerful software "R" in connection with each of the methods exposed in the class.

  • 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

In the News

Activity

In the News

Gaming the System: Are Hedge Fund Managers Talented, or Just Good at Fooling Investors?

Hedge funds are key players in the world's financial markets, but no one knows exactly what they're up to. Critics and supporters tend to share an assumption, however, that hedge funds are run by talented people who merit their hefty management fees. But new research by Wharton statistics professor Dean P. Foster and Brookings Institution senior fellow H. Peyton Young questions that idea, arguing that it's easy for hedge funds to fool their investors into believing the managers are better than they really are. The industry "risks being inundated by managers who are gaming the system ... which could ultimately lead to a collapse in investor confidence," they say.

Knowledge @ Wharton - 2008/04/2
All News