The Annals of Statistics

Rank Tests for Independence for Bivariate Censored Data

Dorota M. Dabrowska

Full-text: Open access

Abstract

The paper discusses statistics that can be used to test whether two failure times, say $X_1$ and $X_2$, are independent. The two variables are subject to right censoring so that what is observed is $Y_i = \min(X_i, Z_i)$ and $\delta_i = I(X_i = Y_i)$, where $(Z_1, Z_2)$ are censoring times independent of $(X_1, X_2)$. Statistics that generalize the Spearman rank correlation and the log-rank correlation are considered, as well as general linear rank statistics. The Chernoff-Savage approach is adopted to show that suitably standardized versions of these statistics are asymptotically normal under both fixed and converging alternatives.

Article information

Source
Ann. Statist., Volume 14, Number 1 (1986), 250-264.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176349853

Digital Object Identifier
doi:10.1214/aos/1176349853

Mathematical Reviews number (MathSciNet)
MR829566

Zentralblatt MATH identifier
0597.62035

JSTOR
links.jstor.org

Subjects
Primary: 62G10: Hypothesis testing
Secondary: 62H20: Measures of association (correlation, canonical correlation, etc.) 62N05: Reliability and life testing [See also 90B25]

Keywords
Bivariate censoring rank correlation statistics

Citation

Dabrowska, Dorota M. Rank Tests for Independence for Bivariate Censored Data. Ann. Statist. 14 (1986), no. 1, 250--264. doi:10.1214/aos/1176349853. https://projecteuclid.org/euclid.aos/1176349853


Export citation