A class of partially linear transformation models for recurrent gap times. Issue 3 (1st February 2018)

• Record Type: Journal Article
• Authors: Han, Miao; Han, Dongxiao; Sun, Liuquan
• Is Part Of: Communications in statistics. Volume 47:Issue 3(2018)
• Page Start: 739
• Page End: 766
• Publication Date: 2018-02-01
• Subjects: Estimating equations -- Gap times -- Local polynomials -- Partially linear transformation models -- Recurrent events.; Primary 62N02 -- Secondary 62G02, 62N01.; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2017.1313986
• Languages: English
• ISSNs: 0361-0926

Abstract

ABSTRACT: In this article, we propose a general class of partially linear transformation models for recurrent gap time data, which extends the linear transformation models by incorporating non linear covariate effects and includes the partially linear proportional hazards and the partially linear proportional odds models as special cases. Both global and local estimating equations are developed to estimate the parametric and non parametric covariate effects, and the asymptotic properties of the resulting estimators are established. The finite-sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a clinic study on chronic granulomatous disease is provided.