Testing quasi-independence for doubly truncated data

Pao-Sheng Shen

Journal of Nonparametric Statistics, 2011, vol. 23, issue 3, 753-761

Abstract: Doubly truncated data appear in a number of applications, including astronomy and survival analysis. Quasi-independence is a common assumption for analysing double-truncated data. To verify this condition, using the approach of Emura and Wang [(2010), ‘Testing Quasi-independence for Truncation Data’, Journal of Multivariate Analysis, 101, 223–293], we propose a class of weighted log-rank-type statistics. The asymptotic distribution theory of the test is presented. The performance of the proposed test is compared with the existing test proposed by Martin and Betensky [(2005), ‘Testing Quasi-independence of Failure and Truncation Via Conditional Kendall's Tau’, Journal of the American Statistical Association, 100, 484–492], by means of Monte Carlo simulations.

Date: 2011
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DOI: 10.1080/10485252.2011.564280

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