Quantile regression for doubly truncated data. Issue 4 (3rd July 2020)

• Record Type: Journal Article
• Authors: Shen, Pao-sheng
• Is Part Of: Statistics. Volume 54:Issue 4(2020)
• Page Start: 649
• Page End: 666
• Publication Date: 2020-07-03
• Subjects: Consistency -- doubly truncated data -- NPMLE -- weighted quantile regression; Primary: 62N99 -- Secondary: 62N02; Mathematical statistics -- Periodicals; 519.505
• DOI: 10.1080/02331888.2020.1772788
• Languages: English
• ISSNs: 0233-1888

Abstract

Abstract : Doubly truncated data arise when the event time of interest T is observed only if it falls within a subject-specific, possibly random, interval [ U, V ] . In this article, we study the problem of fitting a quantile regression model with doubly truncated data. Based on the non-parametric maximum likelihood estimator of K ( t ) = P ( U < t < V ), we proposed a weighted quantile regression estimator. Our method leads to a simple algorithm that can be conveniently implemented with R software. We show that the proposed estimator is consistent and asymptotically normal under appropriate conditions. We evaluate the finite sample performance of the proposed estimators through simulation studies. The proposed method is illustrated using AIDS blood transfusion data.