• Willaman Professor of Statistics
• Head, Department of Statistics
• Director, Center for Likelihood Studies
• Ph.D., University of Washington, 1978
• Personal web site
  

Dr. Lindsay's research is in the following five areas.

Nuisance parameters: When building a statistical model for data collected on a sample of individuals, it is typically necessary to include many parameters to make sure the model is rich enough to approximate the true state of nature. However, the standard methods of statistical analyses have major deficiencies when viewed in this framework.

Mixture models: If the heights of college students in a classroom are sampled, then the distribution of the heights will be bimodal, because the students are a mixture of males and females.

Computer algorithms: There has been an explosion of new ideas and methods in statistics because the computer has made it feasible to be much more sophisticated and realistic in creating models.

Minimum distance and robustness: A long-standing concern in statistical methods has been the sensitivity of the answers to one or more incorrect data points; a robust method is one lacking this deficiency.

Genometrics: One of the most exciting areas of statistical applications is arising through modern biological work on understanding the genome of man and other species.

Dr. Lindsay was a Humboldt Senior Scientist (1991) in Berlin and a Guggenheim Fellow (1996). He is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association. As of 2003, he has supervised 22 Ph.D. dissertations.

Representative publications

B. G. Lindsay, J. Kettenring, and D. O. Siegmund. 2004. A report on the future of Statistics. Statistical Science 19: 387-413.

B. G. Lindsay and A. Qu. 2003. Inference functions and quadratic score tests. Statistical Science 18: 394-410.

C. X. Mao and B. G. Lindsay. 2002. A Poisson model for coverage problems with an application in genomic research. Biometrika 89: 669-682.

A. Qu, B. G. Lindsay and B. Li. 2000. Improving generalized estimating equations using quadratic inference functions. Biometrika 87: 823-836.

M. Markatou, A. Basu, and B. G. Lindsay. 1998. Weighted likelihood equations with bootstrap root search. Journal of the American Statistical Association 93: 740-751.

B. G. Lindsay and B. Li. 1997. On the unconditional optimality of the observed Fisher information as an estimate of squared error loss. Annals of Statistics 25: 2172-2200.

K. Roeder, R. Carroll, and B. G. Lindsay. 1996. A semiparametric mixture approach to case-control studies with errors in covariables. Journal of the American Statistical Association 91: 722-733.
(Winner of the Snedecor Prize)

B. G. Lindsay. 1995. Mixture models: Theory, geometry, and applications. NSF-CBMS Regional Conference Series in Probability and Statistics, Volume 5. Hayward, Calif.: Institute for Mathematical Statistics.

Last updated: 27 April 2005