Nonparametric Estimation of Density and Hazard Rate Functions when Samples are Censored.
作者： W.J. Padgett
DOI: 10.1016/S0169-7161(88)07018-X
关键词: Random variable 、 Multivariate kernel density estimation 、 Density estimation 、 Statistics 、 Nonparametric statistics 、 Mathematics 、 Sample (statistics) 、 M-estimator 、 Econometrics 、 Bayesian probability 、 Estimator
摘要: Publisher Summary This chapter presents the different types of nonparametric density estimates that have been proposed for situation sample data are censored or incomplete. It is interest to be able estimate nonparametrically unknown lifetime random variable from this type without ignoring discarding right-censored information. The various estimators samples include histogram-type estimators, kemel-type maximum likelihood Fourier series and Bayesian estimators. As hazard rate function estimation problem closely related problem, mentioned. Because their computational simplicity other properties, kernel-type emphasized, some examples presented.
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