Biostatistics Seminar speaker, Dr. Bin Nan, University of Michigan, will present, "Fast Estimation of Regression Parameters in a Broken-Stick Model for Longitudinal Data.”
Abstract: Estimation of change-point locations in the broken-stick model has significant applications in modeling important biological phenomena. In this talk, I will present a computationally economical likelihood-based approach for estimating change-point(s) efficiently in both cross-sectional and longitudinal settings. The method, based on local smoothing in a shrinking neighborhood of each change-point, is shown via simulations to be computationally more viable than existing methods that rely on search procedures, with dramatic gains in the multiple change-point case. The proposed estimates are shown to have root-n consistency and asymptotic normality--in particular, they are asymptotically efficient in the cross-sectional setting--allowing us to provide meaningful statistical inference. As the primary and motivating longitudinal application, a two change-point broken-stick model appears to be a good fit to the Michigan Bone Health and Metabolism Study cohort data to describe patterns of change in log estradiol levels, before and after the final menstrual period. A plant growth dataset in the cross-sectional setting is also illustrated. This is a joint work with Rito Das, Mouli Banerjee, and Huiyong Zheng.