Professional Summary
Jack Kalbfleisch is Professor of Biostatistics. He received his Ph.D. in statistics in 1969 from the University of Waterloo. He was Assistant Professor in the Department of Statistics at the State University of New York at Buffalo (1970-73) and on faculty at the University of Waterloo (1973-2002). At Waterloo, he served as Chair of the Department of Statistics and Actuarial Science (1984-1990) and as Dean of the Faculty of Mathematics (1990-1998). He has held visiting appointments as Professor at the University of Washington, the University of Michigan, the University of California at San Francisco, the University of Auckland, and the National University of Singapore. He has worked in various areas of statistics and biostatistics including failure time and survival analysis, likelihood methods of inference, bootstrapping and estimating equations, mixture and mixed effects models and medical applications. Dr. Kalbfleisch is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics. He is also an elected member of the International Statistical Institute, a Fellow of the Royal Society of Canada and Gold Medalist of the Statistical Society of Canada.
Courses Taught
BIOSTAT553: Applied Biostatistics
Syllabus (PDF)
Education
Ph.D., Statistics, University of Waterloo, 1969
M. Math., Statistics, University of Waterloo, 1967
B.Sc., Math and Physics, University of Waterloo, 1966
Research Interest & Projects
One of my primary research interests is in the development of models and methods for analyzing failure time or event history data. Applications of this work arise in many areas including epidemiology, medicine, demography and engineering. In event history data, interest centers on the timing and occurrence of various kinds of events such as, for example, repeated infections or recurrences of disease, or sequences of events that occur through the study period. I have been particularly interested in situations in which only partial data or data subject to sampling bias are available. In many applications, mixture models provide a natural way to describe heterogeneity in a population and I am interested in various aspects of modeling and analyzing mixtures. This research has included work on algorithms for fitting nonparametric mixtures and on methods for testing the order of a finite mixture, a problem arising in various applications in genetics. A third area of interest relates to the use of resampling or bootstrapping techniques when an estimating function or equation forms the basis for inference. By estimating directly the distribution of the estimating functio itself, it is possible to develop methods for resampling that are computationally less demanding than the usual bootstrap methods and, in many instances, also exhibit better properties. Extensions of these methods to time series or martingale type estimating equations are the subject of current investigations.
Selected Publications
Jiang, W. and Kalbfleisch, J.D. (2005, Submitted). Resampling methods for estimating functions with U-statistics structure.
Song, P. X-K., Fan, Y., Kalbfleisch, J. D. (2005, In press). Maximization by Parts in Likelihood Inference. Journal American Statistical Association
Chen, J. and Kalbfleisch, J.D (2004). Modified Likelihood Ratio Test in Finite Mixture Models with a Structural Parameter. Journal of Statistical Planning and Inference, 129, 93-107.
Jewell, N. P. and Kalbfleisch, J. D. (2004). Maximum Likelihood Estimation of Ordered Multinomial Parameters. Biostatistics, 5, 291-306.
Chen, H., Chen, J. and Kalbfleisch, J. D. (2004). Testing for a Finite Mixture Model with Two Components. Journal Royal Statistical Society B, 66, 96-116.
Prentice, R. L. and Kalbfleisch, J. D. (2003). Mixed Continuous and Discrete Cox Models. Lifetime Data Analysis, 9, 195-210.
Kalbfleisch, J. D. and Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data New York: Wiley, 2nd Edition.
Hu, Feifang and Kalbfleisch, J. D. (2000). The Estimating Function Bootstrap (with Discussion). Canadian Journal of Statistics, 30, 449-499.
Professional Affiliations
American Statistical Association
Statistical Society of Canada
Royal Society of Canada
Institute of Mathematical Statistics
International Statistical Institute
International Biometrics Society
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