The Annals of Statistics

Estimation of a monotone density in $s$-sample biased sampling models

Kwun Chuen Gary Chan, Hok Kan Ling, Tony Sit, and Sheung Chi Phillip Yam

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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 $w_{i}$ for $i=1,\ldots,s$. The determination of the monotone maximum likelihood estimator $\hat{g}_{n}$ and its asymptotic distribution, except for the case when $s=1$, has been long missing in the literature due to certain nonstandard structures of the likelihood function, such as nonseparability and a lack of strictly positive second order derivatives of the negative of the log-likelihood function. The existence, uniqueness, self-characterization, consistency of $\hat{g}_{n}$ and its asymptotic distribution at a fixed point are established in this article. To overcome the barriers caused by nonstandard likelihood structures, for instance, we show the tightness of $\hat{g}_{n}$ via a purely analytic argument instead of an intrinsic geometric one and propose an indirect approach to attain the $\sqrt{n}$-rate of convergence of the linear functional $\int w_{i}\hat{g}_{n}$.

Article information

Source
Ann. Statist., Volume 46, Number 5 (2018), 2125-2152.

Dates
Received: February 2016
Revised: May 2017
First available in Project Euclid: 17 August 2018

Permanent link to this document
https://projecteuclid.org/euclid.aos/1534492831

Digital Object Identifier
doi:10.1214/17-AOS1614

Mathematical Reviews number (MathSciNet)
MR3845013

Zentralblatt MATH identifier
06964328

Subjects
Primary: 62G20: Asymptotic properties 62E20: Asymptotic distribution theory
Secondary: 62G08: Nonparametric regression

Keywords
Density estimation empirical process theory Karush–Kuhn–Tucker conditions nonparametric estimation order statistics from multiple samples self-induced characterization shape-constrained problem $s$-sample biased sampling

Citation

Chan, Kwun Chuen Gary; Ling, Hok Kan; Sit, Tony; Yam, Sheung Chi Phillip. Estimation of a monotone density in $s$-sample biased sampling models. Ann. Statist. 46 (2018), no. 5, 2125--2152. doi:10.1214/17-AOS1614. https://projecteuclid.org/euclid.aos/1534492831


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Supplemental materials

  • Supplement to “Estimation of a monotone density in $s$-sample biased sampling models”. In the supplementary paper, we provide the proofs for Propositions 3.1, 3.2, 4.1 and 5.13, Lemmas 5.1, 5.2, 5.4, 5.5, 5.9, 5.11, 5.12, 5.14, 5.15, 6.1, 6.2, 6.3, 6.4 and 6.5, Theorems 1.1 and 6.6. In addition, we also state and prove the fact that the function $\mathcal{\tilde{L}}_{n}$ defined in (3.3) is concave in $\boldsymbol{p}$ in Proposition 8.1, and hence establishes the unique existence of $\hat{g}_{n}$ in Proposition 8.2.