On Self‐Normalization for Censored Dependent Data

16 Pages Posted: 30 Dec 2014

See all articles by Yinxiao Huang

Yinxiao Huang

University of Illinois at Urbana-Champaign - Department of Statistics

Stanislav Volgushev

Ruhr University of Bochum - Faculty of Mathematics

Xiaofeng Shao

University of Illinois at Urbana-Champaign - Department of Statistics

Date Written: January 2015

Abstract

This article is concerned with confidence interval construction for functionals of the survival distribution for censored dependent data. We adopt the recently developed self‐normalization approach (Shao, 2010), which does not involve consistent estimation of the asymptotic variance, as implicitly used in the blockwise empirical likelihood approach of El Ghouch et al. (2011). We also provide a rigorous asymptotic theory to derive the limiting distribution of the self‐normalized quantity for a wide range of parameters. Additionally, finite‐sample properties of the self‐normalization‐based intervals are carefully examined, and a comparison with the empirical likelihood‐based counterparts is made.

Keywords: Censored data, dependence, empirical likelihood, quantile, self‐normalization, survival analysis

Suggested Citation

Huang, Yinxiao and Volgushev, Stanislav and Shao, Xiaofeng, On Self‐Normalization for Censored Dependent Data (January 2015). Journal of Time Series Analysis, Vol. 36, Issue 1, pp. 109-124, 2015, Available at SSRN: https://ssrn.com/abstract=2543848 or http://dx.doi.org/10.1111/jtsa.12096

Yinxiao Huang (Contact Author)

University of Illinois at Urbana-Champaign - Department of Statistics

725 S Wright
Champaign, IL 61820
United States

Stanislav Volgushev

Ruhr University of Bochum - Faculty of Mathematics

D-44780 Bochum
Germany

Xiaofeng Shao

University of Illinois at Urbana-Champaign - Department of Statistics ( email )

725 S Wright
Champaign, IL 61820
United States

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