 A semiparametric model for the cause-specific hazard under risk proportionalitySimon Lo, Ralf Wilke and Takeshi Emura Computational Statistics & Data Analysis, 2024, vol. 195, issue C Abstract: Semiparametric Cox proportional hazards models enjoy great popularity in empirical survival analysis. A semiparametric model for cause-specific hazards under a proportionality restriction across risks is considered, which has desired practical properties such as estimation by partial likelihood and an analytical solution for the copula-graphic estimator. The cause-specific and marginal hazards are shown to share functional form restrictions in this case. The model for the cause-specific hazard can be used for inference about parametric restrictions on the marginal hazard without the risk of misspecifying the latter and without knowing the risk dependence. After the class of parametric marginal hazards has been determined, it can be estimated in conjunction with the degree of risk dependence. Finite sample properties are investigated with simulations. An application to employment duration demonstrates the practicality of the approach. Keywords: Dependent competing risks; Archimedean copula; Identifiability (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:195:y:2024:i:c:s0167947324000379 DOI: 10.1016/j.csda.2024.107953 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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