Partially exact alternatives to regularization in proportional hazards regression models with monotone likelihood

Joint Event on 7^th International Conference on Biostatistics and Bioinformatics & 7^th International Conference on Big Data Analytics & Data Mining

September 26-27, 2018 | Chicago, USA

John E Kolassa

Rutgers University, USA

Keynote: J Comput Sci Syst Biol

Abstract :

Proportional hazards regression models are very commonly used to model time to events in the presence of censoring. In some cases, particularly when sample sizes are moderate and covariates are discrete, maximum partial likelihood estimates are infinite. This lack of finite estimators complicates the use of profile methods for estimating and testing the remaining parameters. This presentation provides a method for inference in such cases. The method builds on similar techniques in use in logistic and multinomial regression and avoids arbitrary regularization.

Biography :

John E Kolassa completed his PhD from the University of Chicago. He worked as a postdoctoral fellow at the IBM TJ Watson Research Center and the University of Rochester. He was an assistant professor at the University of Rochester and is presently a professor at Rutgers. His research is supported by the National Science Foundation and previously by the National Institutes of Health. He is an editor of Stat and is an associate editor of the Journal of the American Statistical Association. He is a fellow of the American Statistical Association, the Institute for Mathematical Statistics and the International Statistical Institute.

E-mail: kolassa@verizon.net