John E Kolassa
Rutgers University, USA
Title: Partially exact alternatives to regularization in proportional hazards regression models with monotone likelihood
Biography
Biography: John E Kolassa
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.