Parametrically guided nonparametric density and hazard estimation with censored data

Talamakrouni, Majda [UCL] Van Keilegom, Ingrid [UCL] El Ghouch, Anouar [UCL]

The parametrically guided kernel smoother is a promising nonparametric estimation approach that aims to reduce the bias of the classical kernel density estimator without increasing its variance. Theoretically, the estimator is unbiased if a correct parametric guide is used, which can never be achieved by the classical kernel estimator even with an optimal bandwidth. The estimator is generalized to the censored data case and used for density and hazard function estimation. The asymptotic properties of the proposed estimators are established and their performance is evaluated via finite sample simulations. The method is also applied to data coming from a study where the interest is in the time to return to drug use.

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Document type	Article de périodique (Journal article) – Article de recherche
Access type	Accès libre
Publication date	2016
Language	Anglais
Journal information	"Computational Statistics & Data Analysis" - Vol. 93, p. 308-323 (2016)
Peer reviewed	yes
Publisher	Elsevier BV (Amsterdam)
issn	0167-9473
e-issn	1872-7352
Publication status	Publié
Affiliation	UCL - SSH/LIDAM/ISBA - Institut de Statistique, Biostatistique et Sciences Actuarielles
Links	http://hdl.handle.net/2078.1/162367[Handle] https://doi.org/10.1016/j.csda.2015.01.009[DOI]

See also
	[boreal:142795]	Parametrically guided nonparametric density and hazard estimation with censored data