Robert A. Stine

Robert A. Stine
  • Professor Emeritus of Statistics

Contact Information

  • office Address:

    444 Jon M. Huntsman Hall
    3730 Walnut Street
    Philadelphia, PA 19104

Research Interests: credit scoring, model selection, pattern recognition and classification, statistical computing and graphics, time series analysis and forecasting

Overview

Education

PhD, Princeton University, 1982
MA, Princeton University, 1979
BS, University of South Carolina, 1977

Recent Consulting

Fraud detection in loan applications; validating models in use for consumer credit default.

Career and Recent Professional Awards

Miller-Sherrerd MBA Core Teaching Award, 2003, 2006, 2007, 2010
David W. Hauck Award for Outstanding Teaching, 2001
Excellence in Teaching Award (Undergraduate Division), 2001, 2004

Academic Positions Held

Wharton: 1979-present (Research Associate, Analysis Center for Evaluation of Energy Modeling and Statistics, 1979-83, Director of Computing Analysis Center, 1979-83).

Previous appointments: Princeton University; University of Michigan; University of South Carolina. Visiting appointment: University of Michigan

Other Positions

Summer Intern, Office of Energy Information Administration, U.S. Department of Energy, 1978

For more information, go to My Personal Page

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Research

Teaching

Past Courses

  • STAT102 - INTRO BUSINESS STAT

    Continuation of STAT 101. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications. This course may be taken concurrently with the prerequisite with instructor permission.

  • STAT399 - INDEPENDENT STUDY

    Written permission of instructor and the department course coordinator required to enroll in this course.

  • STAT405 - STAT COMPUTING WITH R

    The goal of this course is to introduce students to the R programming language and related eco-system. This course will provide a skill-set that is in demand in both the research and business environments. In addition, R is a platform that is used and required in other advanced classes taught at Wharton, so that this class will prepare students for these higher level classes and electives.

  • STAT430 - PROBABILITY

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

  • 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. A one-year course in calculus is recommended.

  • 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.

  • STAT613 - REGR ANALYSIS FOR BUS

    This course provides the fundamental methods of statistical analysis, the art and science if extracting information from data. The course will begin with a focus on the basic elements of exploratory data analysis, probability theory and statistical inference. With this as a foundation, it will proceed to explore the use of the key statistical methodology known as regression analysis for solving business problems, such as the prediction of future sales and the response of the market to price changes. The use of regression diagnostics and various graphical displays supplement the basic numerical summaries and provides insight into the validity of the models. Specific important topics covered include least squares estimation, residuals and outliers, tests and confidence intervals, correlation and autocorrelation, collinearity, and randomization. The presentation relies upon computer software for most of the needed calculations, and the resulting style focuses on construction of models, interpretation of results, and critical evaluation of assumptions.

  • STAT621 - ACC REGRESSION ANALYSIS

    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. Recent exposure to the theory and practice of regression modeling is recommended.

  • 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. Prerequisite: two courses at the statistics 400 or 500 level or permission from instructor.

  • STAT705 - STAT COMPUTING WITH R

    The goal of this course is to introduce students to the R programming language and related eco-system. This course will provide a skill-set that is in demand in both the research and business environments. In addition, R is a platform that is used and required in other advanced classes taught at Wharton, so that this class will prepare students for these higher level classes and electives.

  • STAT724 - TEXT ANALYTICS

    This course introduces methods for the analysis of unstructured data, focusing on statistical models for text. Techniques include those for sentiment analysis, topic models, and predictive analytics. Course includes topics from natural language processing (NLP), such as identifying parts of speech, parsing sentences (e.g., subject and predicate), and named entity recognition (people and places). Unsupervised techniques suited to feature creation provide variables suited to traditional statistical models (regression) and more recent approaches (regression trees). Examples that span the course illustrate the success of text analytics. Hierarchical generating models often associated with nonparametric Bayesian analysis supply theoretical foundations. Students should be familiar with regression models at the level of STAT 613 and the R statistics language at the level of STAT 705. Familiarity with the R-Studio development environment is presumed, as well as common R packages such as stringr, dplyr and ggplot. Those with more knowledge of Statistics, such as from STAT 722, or computing skills will benefit. The predominant software used in the course is R, with bits of JMP when helpful for interactive illustration. Familiarity with basic probability models is helpful but not presumed.

  • STAT899 - INDEPENDENT STUDY

    Written permission of instructor, the department MBA advisor and course coordinator required to enroll.

  • STAT995 - DISSERTATION

  • STAT999 - INDEPENDENT STUDY

    Written permission of instructor and the department course coordinator required to enroll.

Awards and Honors

  • Wharton MBA Excellence in Teaching Award, 2012
  • Helen Kardon Moss Anvil Award for MBA Teaching, 2011
  • Wharton MBA Excellence in Teaching Award, 2009
  • Wharton MBA Excellence in Teaching Award, 2006-2007
  • Excellence in Teaching Award (Undergraduate Division), 2004
  • Miller-Sherrerd Wharton MBA Core Teaching Award, 2003
  • David W. Hauck Award for Outstanding Teaching, 2001
  • Excellence in Teaching Award (Undergraduate Division), 2001
  • Miller-Sherrerd Wharton MBA Core Teaching Award, 1999
  • Wharton MBA Core Curriculum Teaching Award, 1996
  • Excellence in Teaching Award (Undergraduate Division), 1995
  • Miller-Sherrerd Wharton MBA Core Teaching Award, 1994-1995

Activity

Latest Research

Sivan Aldor-Noiman, Lawrence D. Brown, Emily Fox, Robert A. Stine (2017), Spatio-temporal Low Count Processes with Application to Violent Crime Events, Statistica Sinica, 26, pp. 1587-1610.
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In the News

More Savings, Less Plastic: Consumer Credit after the Crisis

The harsh economic downturn that has chastened credit-happy consumers, along with increased scrutiny by regulators, will force card issuers to rethink their business models as the economy begins to recover, according to Wharton faculty and credit industry analysts.

Knowledge @ Wharton - 7/8/2009
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Sivan Aldor-Noiman, GR’12, came to Wharton Doctoral Programs from a small statistics program in Israel, and found herself in a dynamic center of intellectual life.Read More

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