時 間:112年9月26日(星期二下午14:10~15:00)
地 點:管理學院新大樓M240
主講人:張志浩 教授 (政治大學 統計學系)
講 題:Estimation of Threshold Boundary Regression Models
Abstract
This talk considers the threshold boundary regression (TBR) model for sample splitting. The TBR model accommodates covariates in both the regression and threshold functions. The threshold function is allowed to be a nonlinear function of multiple covariates, constituting a hyperplane to describe data dynamics in two different states. We propose TBR-WSVM, a two-stage method that incorporates the weighted support vector machine (WSVM) and least-squares (LS) methods to estimate the TBR model. We conduct several simulation experiments to investigate the finite sample performance of the TBR-WSVM estimator. Compared with two recently proposed methods, TBR-WSVM enjoys three advantages: (i) threshold parameters need not be prefixed with nonzero values, (ii) threshold parameter ranges need not be specified, and (iii) the threshold boundary can be non-linearly estimated. Finally, we apply the TBR model to a real data analysis.
Keywords: Iterative estimators, least-squares estimation, support vector machine, threshold estimation.