Dai, H and Restaino, M and Wang, H (2016) 'A class of nonparametric bivariate survival function estimators for randomly censored and truncated data.' Journal of Nonparametric Statistics, 28 (4). pp. 736-751. ISSN 1048-5252

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Abstract

© 2016, © American Statistical Association and Taylor & Francis 2016. 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.

Item Type:	Article
Uncontrolled Keywords:	Bivariate survival function; random censoring; random truncation; correlated failure times; data transformation method
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Science and Health Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor:	Elements
Depositing User:	Elements
Date Deposited:	11 Aug 2016 15:24
Last Modified:	15 Jan 2022 00:43
URI:	http://repository.essex.ac.uk/id/eprint/17362

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