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Jeffrey S. Simonoff

Jeffrey S. Simonoff

Joined Stern 1980

Leonard N. Stern School of Business
Kaufman Management Center
44 West Fourth Street, 8-54
New York, NY 10012

E-mail jsimonof@stern.nyu.edu
Personal website

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Biography

Jeffrey Simonoff is the Professor of Statistics at New York University Stern School of Business. He teaches courses in statistics and data analysis, and regression and multivariate data analysis.

Dr. Simonoff has been with Stern for more than 30 years. His primary areas of research include categorical data, outlier identification, smoothing methods, the analysis of large data sets, and the application of statistics to problems in business and science. He has written several books including Smoothing Methods in Statistics (subsequently released in a special edition in the People's Republic of China, and also translated into Japanese), A Casebook for a First Course in Statistics and Data Analysis, Analyzing Categorical Data, Nonprofit Trusteeship in Different Contexts, and Handbook of Regression Analysis. He co-edited The SAGE Handbook of Multilevel Modeling. He is a Fellow of the American Statistical Association and of the Institute of Mathematical Statistics, and an Elected Member of the International Statistical Institute. His work has been published in numerous journals including The Journal of Futures Markets, Journal of the American Statistical Association, Journal of the Royal Statistical Society, The Journal of Business, Journal of Machine Learning Research, Machine Learning, and Risk Analysis. In addition, Dr. Simonoff is Co-Editor of Statistical Modelling: An International Journal, and a member of the Editorial Board of Foundations and Trends in Econometrics. He has chaired or served on many Stern and University task forces and committees, and was a finalist for Stern School Professor of the Year in 1997.

Dr. Simonoff received his Bachelor of Science from SUNY Stony Brook, and both a Master of Philosophy and Doctor of Philosophy in statistics from Yale University.

Research Interests

  • Smoothing methods
  • Categorical data analysis
  • Outlier identification
  • Robust estimation
  • Analysis of large data sets

Courses Taught

  • Regression and Multivariate Data Analysis
  • Statistics and Data Analysis

Academic Background

Ph.D., Statistics, 1980
Yale University

M.Phil., Statistics, 1978
Yale University

B.S., Applied Mathematics & Statistics, Computer Science, 1976
State University of New York, Stony Brook

Awards & Appointments

 
Institute of Mathematical Statistics Fellow 2003
International Statical Institute Elected Member 1998
American Statistical Association Fellow 1996