• Willaman Professor of Statistics
• Head, Department of Statistics
• Ph.D., University of Washington, 1978
  

Current CV

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. He was selected as the Fisher lecturer for COPSS in 2010, one of the premier honors in statistics. As of 2010, he had supervised 27 Ph.D. dissertations.

Representative publications

Liu, J. and Lindsay, B. G. 2009. Building and using semiparametric tolerance regions for parametric models.  Annals of Statistics 37: 3644-3659.

Yao, W. and Lindsay, B. G. 2009. Bayesian Mixture Labeling by Highest Posterior Density. Journal of the American Statistical Association 104(486): 758-767.

Lindsay, B. G. and Liu, J. 2009. Model assessment tools for a model false world.  Statistical Science 24: 303-318.

Lindsay, B. G., Markatou, M., Ray, S. R., Yang, K., and Chen, S. C. 2008. Quadratic distances on probabilities: a unified foundation. Annals of Statistics 36: 983-1006.

Li, J, Ray, S. R., and Lindsay, B. G. 2007. A nonparametric approach to clustering via mode identification.  Journal of Machine Learning Research 8: 1687-1723.

Chen, S. C. and Lindsay, B. G. 2006. Building mixture trees from binary sequence data. Biometrika 93: 843-860.

Cui, Liying, Wall, P. Kerr, Leebens-Mack, James H., Lindsay, Bruce G., Soltis, Douglas E., Doyle, Jeff J., Soltis, Pamela S., Carlson, John E., Arumuganathan, Kathiravetpilla, Barakat, Abdelali, Albert, Victor A., Ma, Hong, dePamphilis, Claude W. 2006.  Widespread genome duplication throughout the history of flowering plants.  Genome Research 16: 738 – 749.

Lindsay, B. G. and Ray, S. 2005. The topography of multivariate normal mixtures. Annals of Statistics 33: 2042-2065.

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

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

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

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

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

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

Roeder, K., Carroll, R., and Lindsay, B. G. 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)

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

Last updated: 16 July 2010