Estimation and Hypothesis Testing for Four Types of Change Point Regression Models with Non-Thresholded Covariates
Abstract
Change point models are a diverse set of non-regular models that all depend on change points or thresholds. Many software implementations exist for change point models that are aimed at detecting structural changes in a time series. Motivated by non-time series biometrical applications, the R package \textit{chngpt} provides estimation and hypothesis testing functionalities for four variants of change point regression models that allow covariates not subject to thresholding. We illustrate its usage with a real example from the study of immune responses associated with reduction in mother-to-child-transmission of HIV-1 viruses.
This paper has been withdrawn.