Fast Bootstrap Confidence Intervals for Continuous Threshold Linear Regression.

ABSTRACT: Continuous threshold regression is a common type of nonlinear regression that is attractive to many practitioners for its easy interpretability. More widespread adoption of thresh-old regression faces two challenges: (i) the computational complexity of fitting threshold regression models and (ii) obtaining correct coverage of confidence intervals under model misspecification. Both challenges result from the non-smooth and non-convex nature of the threshold regression model likelihood function. In this paper we first show that these two issues together make the ideal approach for making model-robust inference in continuous threshold linear regression an impractical one. The need for a faster way of fitting continuous threshold linear models motivated us to develop a fast grid search method.

SUBMITTER: Fong Y 

PROVIDER: S-EPMC6713448 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature