A class of nonparametric bivariate survival function estimators for randomly censored and truncated data. Issue 4 (1st October 2016)

• Record Type: Journal Article
• Authors: Dai, Hongsheng; Restaino, Marialuisa; Wang, Huan
• Is Part Of: Journal of nonparametric statistics. Volume 28:Issue 4(2016)
• Page Start: 736
• Page End: 751
• Publication Date: 2016-10-01
• Subjects: Bivariate survival function -- random censoring -- random truncation -- correlated failure times -- data transformation method; Primary: 62N01 -- Secondary: 62G05; Nonparametric statistics -- Periodicals; 519.5
• DOI: 10.1080/10485252.2016.1225734
• Languages: English
• ISSNs: 1048-5252

Abstract

ABSTRACT: This paper proposes a class of nonparametric estimators for the bivariate survival function estimation under both random truncation and random censoring. In practice, the pair of random variables under consideration may have certain parametric relationship. The proposed class of nonparametric estimators uses such parametric information via a data transformation approach and thus provides more accurate estimates than existing methods without using such information. The large sample properties of the new class of estimators and a general guidance of how to find a good data transformation are given. The proposed method is also justified via a simulation study and an application on an economic data set.