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Wavelet Analysis of Change Points in Nonparametric Hazard Rate Models Under Random Censorship

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  • Jingle Wang
  • Ming Zheng
  • Wen Yu

Abstract

In this article, we consider detection and estimation of change points in nonparametric hazard rate models. Wavelet methods are utilized to develop a testing procedure for change points detection. The asymptotic properties of the test statistic are explored. When there exist change points in hazard function, we also propose estimators for the number, the locations, and the jump sizes of the change points. The asymptotic properties of these estimators are systematically derived. Some simulation examples are conducted to assess the finite sample performance of the proposed approach and to make comparisons with some existing methods. A real data analysis is provided to illustrate the new approach.

Suggested Citation

  • Jingle Wang & Ming Zheng & Wen Yu, 2014. "Wavelet Analysis of Change Points in Nonparametric Hazard Rate Models Under Random Censorship," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(9), pages 1956-1978, May.
    • Handle: RePEc:taf:lstaxx:v:43:y:2014:i:9:p:1956-1978
    DOI: 10.1080/03610926.2012.681416
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    Cited by:

    1. Alessandra R. Brazzale & Helmut Küchenhoff & Stefanie Krügel & Tobias S. Schiergens & Heiko Trentzsch & Wolfgang Hartl, 2019. "Nonparametric change point estimation for survival distributions with a partially constant hazard rate," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 301-321, April.

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