Nonparametric Estimation in Uniform Deconvolution and Interval Censoring

Fuente: arXiv
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Main Authors: Groeneboom, Piet, Jongbloed, Geurt
Format: Preprint
Published: 2025
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author Groeneboom, Piet
Jongbloed, Geurt
author_facet Groeneboom, Piet
Jongbloed, Geurt
contents In the uniform deconvolution problem one is interested in estimating the distribution function $F_0$ of a nonnegative random variable, based on a sample with additive uniform noise. A peculiar and not well understood phenomenon of the nonparametric maximum likelihood estimator in this setting is the dichotomy between the situations where $F_0(1)=1$ and $F_0(1)<1$. If $F_0(1)=1$, the MLE can be computed in a straightforward way and its asymptotic pointwise behavior can be derived using the connection to the so-called current status problem. However, if $F_0(1)<1$, one needs an iterative procedure to compute it and the asymptotic pointwise behavior of the nonparametric maximum likelihood estimator is not known. In this paper we describe the problem, connect it to interval censoring problems and a more general model studied in Groeneboom (2024) to state two competing naturally occurring conjectures for the case $F_0(1)<1$. Asymptotic arguments related to smooth functional theory and extensive simulations lead us to to bet on one of these two conjectures.
format Preprint
id arxiv_https___arxiv_org_abs_2504_14555
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Nonparametric Estimation in Uniform Deconvolution and Interval Censoring
Groeneboom, Piet
Jongbloed, Geurt
Statistics Theory
62G05, 62N01
In the uniform deconvolution problem one is interested in estimating the distribution function $F_0$ of a nonnegative random variable, based on a sample with additive uniform noise. A peculiar and not well understood phenomenon of the nonparametric maximum likelihood estimator in this setting is the dichotomy between the situations where $F_0(1)=1$ and $F_0(1)<1$. If $F_0(1)=1$, the MLE can be computed in a straightforward way and its asymptotic pointwise behavior can be derived using the connection to the so-called current status problem. However, if $F_0(1)<1$, one needs an iterative procedure to compute it and the asymptotic pointwise behavior of the nonparametric maximum likelihood estimator is not known. In this paper we describe the problem, connect it to interval censoring problems and a more general model studied in Groeneboom (2024) to state two competing naturally occurring conjectures for the case $F_0(1)<1$. Asymptotic arguments related to smooth functional theory and extensive simulations lead us to to bet on one of these two conjectures.
title Nonparametric Estimation in Uniform Deconvolution and Interval Censoring
topic Statistics Theory
62G05, 62N01
url https://arxiv.org/abs/2504.14555