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 article we present a computationally economical likelihood-based approach for estimating change-point(s) efficiently in both cross-sectional and longitudinal settings. Our 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 [Formula: see text]-consistency and asymptotic normality - in particular, they are asymptotically efficient in the cross-sectional setting - allowing us to provide meaningful statistical infer

SUBMITTER: Das R 

PROVIDER: S-EPMC5353362 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature