A note on an iterative algorithm for nonparametric estimation in biased sampling models

Ori Davidov; George Iliopoulos

doi:10.1016/j.csda.2008.08.011

A note on an iterative algorithm for nonparametric estimation in biased sampling models

Ori Davidov, George Iliopoulos

Department of Statistics

Research output: Contribution to journal › Article › peer-review

Abstract

A simple iterative estimation procedure for computing the nonparametric maximum likelihood estimator (NPMLE) in biased sampling models is discussed and studied in detail. A proof of convergence is provided. Numerical experiments show that the algorithm is significantly faster in terms of CPU time compared with the standard procedure.

Original language	English
Pages (from-to)	620-624
Number of pages	5
Journal	Computational Statistics and Data Analysis
Volume	54
Issue number	3
DOIs	https://doi.org/10.1016/j.csda.2008.08.011
State	Published - 1 Mar 2010

Bibliographical note

Funding Information:
This research was supported by the Israel Science Foundation grant No. 1049/06.

ASJC Scopus subject areas

Statistics and Probability
Computational Mathematics
Computational Theory and Mathematics
Applied Mathematics

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10.1016/j.csda.2008.08.011

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title = "A note on an iterative algorithm for nonparametric estimation in biased sampling models",

abstract = "A simple iterative estimation procedure for computing the nonparametric maximum likelihood estimator (NPMLE) in biased sampling models is discussed and studied in detail. A proof of convergence is provided. Numerical experiments show that the algorithm is significantly faster in terms of CPU time compared with the standard procedure.",

author = "Ori Davidov and George Iliopoulos",

note = "Funding Information: This research was supported by the Israel Science Foundation grant No. 1049/06.",

year = "2010",

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AU - Iliopoulos, George

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PY - 2010/3/1

Y1 - 2010/3/1

N2 - A simple iterative estimation procedure for computing the nonparametric maximum likelihood estimator (NPMLE) in biased sampling models is discussed and studied in detail. A proof of convergence is provided. Numerical experiments show that the algorithm is significantly faster in terms of CPU time compared with the standard procedure.

AB - A simple iterative estimation procedure for computing the nonparametric maximum likelihood estimator (NPMLE) in biased sampling models is discussed and studied in detail. A proof of convergence is provided. Numerical experiments show that the algorithm is significantly faster in terms of CPU time compared with the standard procedure.

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DO - 10.1016/j.csda.2008.08.011

M3 - Article

AN - SCOPUS:70549094805

SN - 0167-9473

VL - 54

SP - 620

EP - 624

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

IS - 3

ER -

A note on an iterative algorithm for nonparametric estimation in biased sampling models

Abstract

Bibliographical note

ASJC Scopus subject areas

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