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Hazard regression with noncompactly supported bases
Author(s) -
Brunel Elodie,
Comte Fabienne
Publication year - 2021
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11619
Subject(s) - estimator , generalization , mathematics , nonparametric statistics , hazard , regression , nonparametric regression , statistics , function (biology) , hazard ratio , basis (linear algebra) , regression analysis , upper and lower bounds , econometrics , confidence interval , mathematical analysis , chemistry , geometry , organic chemistry , evolutionary biology , biology
In this article, we consider the problem of nonparametric hazard rate estimation in the presence of right‐censored observations. We provide a generalized risk bound for a regression‐type nonparametric estimator of the hazard function of interest. Under adequate integrability conditions, our bound is a generalization of estimation strategies specific to compactly supported bases to bases that are not necessarily compactly supported. We show that it encompasses previous compact‐support results and interestingly represents hazard rates as combinations of gamma functions. We discuss the model selection method, which comes out from the new terms of the risk bounds, and compare the performance of the new estimator to that of previous ones, when using a noncompact Laguerre basis. A real data example is also presented.