Research Interests: applications in the law, operations management and marketing, applied probability, bootstrap, complex sample surveys, density estimation, grouped data, observational studies, worst case analysis of heuristics
PhD, Harvard University, 1974
MS, Harvard University, 1973
BS, MS, Massachusetts Institute of Technology, 1972
Consultant to several major companies in the areas of data analysis, statistical methodology, mathematical modeling, and marketing research.
Fellow, American Statistical Association, 1992
Alpha Kappa Psi Award, Journal of Marketing, 1991
Lindback Award for Distinguished Teaching, University of Pennsylvania, 1978
Helen Kardon Moss Anvil Award for Teaching Excellence in the Graduate Division, 1977
Undergraduate Division Excellence in Teaching Award, 1991, 1995, 1996
David W. Hauck Award for Outstanding Teaching in the Undergraduate Division,1996
Wharton: 1974-present (Chairperson, Statistics Department, 2002-2008; named Robert Steinberg Professor, 1996).
Ruth Heller, Nilanjan Chatterjee, Abba M. Krieger, Jianxin Shi (2018), Post-selection Inference Following Aggregate Level Hypothesis Testing in Large Scale Genomic Data, Journal of the American Statistical Association, (forthcoming).
Andreas Buja, Natalia Volfovsky, Abba M. Krieger, Catherine Lord, Michael Wigler, Ivan Iossifov (2018), Damaging De Novo Mutations Diminish Motor Skills in Children on the Autism Spectrum, Proceedings of the National Academy of Sciences of the United States of America, (forthcoming).
Hoameng Ung, Steven N. Baldassano, Hank Bink, Abba M. Krieger, Shawniqua Williams, Flavia Vitale, Chengyuan Wu, Dean Freestone, Ewan Nurse, Kent Leyde, Kathryn A. Davis, Mark Cook, Brian Litt (2017), Intracranial EEG Fluctuates Over Months After Implanting Electrodes in Human Brain, Journal of Neural Engineering, 14 (5).
Hoameng Ung, Christian Cazares, Ameya Nanivadekar, Lohith Kini, Joost Wagenaar, Danielle Becker, Abba M. Krieger, Timothy Lucas, Brian Litt, Kathryn A. Davis (2017), Interictal Epileptiform Activity Outside the Seizure Onset Zone Impacts Cognition, Brain, a Journal of Neurology, 140 (8), pp. 2157-2168.
Kenny Ye, Ivan Iossifov, Dan Levy, Boris Yamrom, Andreas Buja, Abba M. Krieger, Michael Wigler (2017), Measuring Shared Variants in Cohorts of Discordant Siblings with Applications to Autism, Proceedings of the National Academy of Sciences of the United States of America PNAS, Proceedings of the National Academy of Sciences, 114 (27), pp. 7073-7076.
Adam Kapelner, Abba M. Krieger, William J. Blanford (2016), Optimal Experimental Designs for Estimating Henry’s Law Constants via the Method of Phase Ratio Variation, Journal of Chromatography A, 1468, pp. 183-191.
Abba M. Krieger, Leonard Lodish, Ye Hu (2016), An Integrated Procedure to Pretest and Select Advertising Campaigns for TV and Beyond, Journal of Customer Needs and Solutions, 3 (2), pp. 81-93. 10.1007/s40547-016-0065-4
Andreas Buja, Abba M. Krieger, Edward I. George, “A Visualization Tool for Mining Large Correlation Tables: The Association Navigator”. In Handbook of Big Data, edited by Peter Bühlmann, Petros Drineas, Michael Kane, Mark van der Laan, (CRC Press, 2016), pp. 73-102
Adam Kapelner and Abba M. Krieger (2014), Matching on-the-fly: Sequential allocation with higher power and efficiency, Biometrics, 70 (2), pp. 378-388.
Abstract: We propose a dynamic allocation procedure that increases power and efficiency when measuring an average treatment effect in fixed sample randomized trials with sequential allocation. Subjects arrive iteratively and are either randomized or paired via a matching criterion to a previously randomized subject and administered the alternate treatment. We develop estimators for the average treatment effect that combine information from both the matched pairs and unmatched subjects as well as an exact test. Simulations illustrate the method's higher efficiency and power over several competing allocation procedures in both simulations and in data from a clinical trial.
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.
Continuation of STAT 101. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications.
Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory.
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.
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.
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.
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.
This seminar takes place over two semesters and provides students with the skills to perform their own research under the guidance of a Wharton faculty member. At the conclusion of the fall semester, students will produce a thesis proposal including literature review, significance of the research, methodology, and exploratory data if relevant. Throughout the fall semester faculty guests from a range of disciplines will present on their research in class, highlighting aspects that are relevant to the work students are engaging in at that point. During the second semester, students will collect and analyze data and write up the results in close collaboration with their faculty mentor. At the end of the spring semester, each student will present their research in a video presentation. Throughout the course, students will work individually, in small groups, and under the mentorship of a Wharton faculty member. The goal is to becomes capable independent researchers who incorporate feedback and critical (self-) analysis to take their research to the next level.
“An Analysis of Real World TV Advertising Tests: A 15-Year Update”, from JAR volume 47, issue 3, has been voted by the Journal of Advertising Research Editorial Board as the JAR Best Paper of 2007.The JAR Best Paper Prize was introduced this year to recognize the contribution of JAR authors to furthering the industry’s knowledge of advertising research.
1991, 1995, 1996
The Wharton School recently lost two of its most talented and respected faculty members, individuals whose accomplishments shaped the future of their respective disciplines. Emeritus professors Paul Kleindorfer and Paul Green died near the beginning of the academic year -- Kleindorfer on August 24 and Green on September 21. Both are praised by friends and colleagues for their contributions not only to their own academic pursuits, but to Wharton's expansion into new areas of business education over the past few decades.Knowledge @ Wharton - 2012/10/10