Paper Title
A METHOD FOR SWITCHING WEIBULL REGRESSIONS

Abstract
Abstract - The switching regression model is used to determine the relationship between variables from several unknown latent groups. It has been a longstanding topic in the research of model-based clustering and has been widely applied in many areas such as econometrics, biology, epidemiology, and engineering. The Weibull distribution is well-known in the applications of survival analysis, life insurance, and reliability analysis. There is a great demand for research on Weibull regression models, especially the research on the switching Weibull regressions. However, in the literature, there are few studies on switching regression models of Weibull distributions. In this study, weintroducea switching Weibull regression model with an algorithm based on the expectation and maximization algorithm for making estimations (EM). TheEM-based algorithm is used most often for deriving the maximum likelihood estimates. It is well known that, the performance of the EM algorithm heavily depends on the choice of initial values and its convergence speed is relatively low. Hence, in using EM for switching Weibull regressions, the selection of good initial values or the summarization of the outcomes from different sets of initial values are important. In this research, we propose switching regression models for the Weibull distribution, develop EM-based algorithms to estimate model parameters. Several simulation results show the proposed methods are feasible in modeling the Weibull distributed data. A real example is used to show the practicability of the proposed method. Keywords - Switching Regressions, Expectation And Maximization Algorithm, Weibull Distribution, Weibull Switching Regressions