Bus Stat Doctoral Biblio

University of Illinois at Chicago
College of Business Administration
PhD Program in Business Administration
Business Statistics Area-of-Inquiry

Bibliography for Business Statistics Doctoral Students

The Qualifying Exam is based on Hogg, McKean, and Craig's book on mathematical statistics and Greene's book on econometrics. Note that this does not mean that the questions on the exams will be taken directly from these books. Indeed, part of the growth from student to professional is becoming knowledgeable about the field of statistics in general. At the time of the Qualifying Exam, this means probability, applied statistics, mathematical statistics and econometrics at the level of Hogg, McKean and Craig and Greene.

The books by Hogg, McKean and Craig and Greene are listed below with other recommended books of special interest. Though, in response to queries, several books are listed in each category, it is certainly not necessarily to have looked at all of them ! Thorough acquaintance with one in each category should be sufficient.

Econometrics

William H. Greene. Econometric Analysis. 5th ed.

Fumio Hayashi. Econometrics. Princeton University Press, 2000.

Peter Kennedy. A Guide to Econometrics. 5th ed. The MIT Press, Cambridge, MA, 2003.

Mathematical Statistics

Harald Cramér. Mathematical Methods of Statistics. Princeton University Press, Princeton, NJ, 1948.
Still an excellent read, but also of historical interest.

George Casella and Roger L. Berger. Statistical Inference. 2nd ed. Wadsworth Pub. Co., 2002.

Morris DeGroot and Mark Schervish. Mathematical Statistics, 3rd. ed. Prentice Hall, Upper Saddle River, NJ, 2003.

Robert V. Hogg, Joseph W. McKean and Allen T. Craig. Introduction to Mathematical Statistics. 6th ed. Pearson Prentice Hall, Upper Saddle River, NJ, 2005. (Available 2004.) ISBN # 0-13-008507-3.

Bernard W. Lindgren. Statistical Theory. 4th ed. Chapman & Hall, New York, 1993.

Bayesian Viewpoint

Roderick J. Little. "Calibrated Bayes: A Bayes / Frequentist Roadmap." The American Statistician, Volume 60, Number 3, August 2006, pp. 213-223.

Donald A. Berry. Statistics: A Bayesian Perspective. Duxbury, 1996.

Andrew Gelman, John B. Carlin, Hal S. Stern, and Donald B. Rubin. Bayesian Data Analysis. 2nd ed. Chapman & Hall/CRC, 2004.

Probability

A. Papoulis. Probability, Random Variables, and Stochastic Processes. McGraw-Hill.

Emanuel Parzen. Modern Probability Theory and its Applications. Wiley.

Stochastic Processes

Emanuel Parzen. Stochastic Processes. Holden-Day, San Francisco, 1962.

Sheldon M. Ross. Applied Probability Models with Optimization Applications. Holden-Day, San Francisco, 1970.