Model-robust inference for continuous threshold regression models.

ABSTRACT: We study threshold regression models that allow the relationship between the outcome and a covariate of interest to change across a threshold value in the covariate. In particular, we focus on continuous threshold models, which experience no jump at the threshold. Continuous threshold regression functions can provide a useful summary of the association between outcome and the covariate of interest, because they offer a balance between flexibility and simplicity. Motivated by collaborative works in studying immune response biomarkers of transmission of infectious diseases, we study estimation of continuous threshold models in this article with particular attention to inference under model misspecification. We derive the limiting distribution of the maximum likelihood estimator, and propose

SUBMITTER: Fong Y 

PROVIDER: S-EPMC5435560 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature