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ESTIMATION OF A MONOTONE DENSITY IN S-SAMPLE BIASED SAMPLING MODELS.


ABSTRACT: We study the nonparametric estimation of a decreasing density function g 0 in a general s-sample biased sampling model with weight (or bias) functions wi for i = 1, …, s. The determination of the monotone maximum likelihood estimator ?n and its asymptotic distribution, except for the case when s = 1, has been long missing in the literature due to certain non-standard structures of the likelihood function, such as non-separability and a lack of strictly positive second order derivatives of the negative of the log-likelihood function. The existence, uniqueness, self-characterization, consistency of ?n and its asymptotic distribution at a fixed point are established in this article. To overcome the barriers caused by non-standard likelihood structures, for instance, we show the tightness of ?n via a purely analytic argument instead of an intrinsic geometric one and propose an indirect approach to attain the n -rate of convergence of the linear functional ? wi ?n.

SUBMITTER: Chan KCG 

PROVIDER: S-EPMC6251412 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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ESTIMATION OF A MONOTONE DENSITY IN <i>S</i>-SAMPLE BIASED SAMPLING MODELS.

Chan Kwun Chuen Gary KCG   Ling Hok Kan HK   Sit Tony T   Yam Sheung Chi Phillip SCP  

Annals of statistics 20180817 5


We study the nonparametric estimation of a decreasing density function <i>g</i> <sub>0</sub> in a general <i>s</i>-sample biased sampling model with weight (or bias) functions <i>w<sub>i</sub></i> for <i>i</i> = 1, …, <i>s</i>. The determination of the monotone maximum likelihood estimator <i>ĝ<sub>n</sub></i> and its asymptotic distribution, except for the case when <i>s</i> = 1, has been long missing in the literature due to certain non-standard structures of the likelihood function, such as n  ...[more]

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