Department of Statistics, The Ohio State University
Statistics and Biostatistics Colloquium Series

We consider partly linear transformation models applied to current status data, which arise naturally in cancer and HIV studies. The unknown quantities are the transformation function, a linear regression parameter, and a nonparametric regression effect. It is shown that the penalized MLE for the regression parameter is asymptotically normal and efficient and converges at the parametric rate, although the penalized MLE for the transformation function and nonparametric regression effect are only n^(1/3) consistent. Inference for the regression parameter based on the weighted bootstrap is investigated. Under mild regularity conditions, similar results hold for the penalized least squares estimate. We also study computational issues and demonstrate the proposed methodology with a simulation study and analysis of the California Partner Study data. The transformation models and partly linear regression terms, coupled with new estimation and inference techniques, provide flexible alternatives to the Cox model for current status data analysis. This study is joint with Dr. Michael R. Kosorok, University of Wisconsin.

Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.