Biography: Peter E. Rossi
Peter E. Rossi is James Collins Professor of Marketing, Economics and Statistics at the Anderson School of Management, UCLA. He received his PhD from University of Chicago and BA from Oberlin College. He has published widely in marketing, economics, statistics and econometrics including Quantitative Marketing and Economics, Marketing Science, Journal of Marketing Research, American Economic Review, Journal of the American Statistical Association, Econometrica, Journal of Political Economy, Journal of Econometrics, Biometrika, Journal of Business and Economic Statistics, Rand Journal of Economics, and Journal of Economic Theory. These articles have more than 14,000 Google Scholar cites. He is a co-author of Bayesian Statistics and Marketing, John Wiley Series in Probability and Statistics (2005) and author of Bayesian Semi and Non-parametric Methods in Marketing and Micro-Econometrics, Princeton University Press (2013). He is author of the contributed R package, bayesm, which is part of the R core and implements many methods useful in marketing and micro-econometrics.
Professor Rossi founded the Kilts Center for Marketing, Booth School of Business, University of Chicago while on faculty there.
His areas of research interest include pricing and promotion, target marketing, direct marketing, micro-marketing, limited dependent variable models and Bayesian statistical methods. A fellow of the American Statistical Association and the Journal of Econometrics, he is senior editor, Marketing Science, founding editor, Quantitative Marketing and Economics, past Associate Editor for Journal of the American Statistical Association, Journal of Econometrics, and Journal of Business and Economic Statistics.
His work in the area of target marketing presaged many of the developments in targeting today as practiced in electronic couponing and by web-based retailers. His work in data-based pricing and methods for estimation of high-dimensional demand systems influenced the development of analytic pricing tools. His work on Bayesian Hierarchical choice models created the most widely used methods for analysis of choice and conjoint data used today and embodied in software from CRAN, SAS, and Sawtooth Software.
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