Semi-parametric mixture models with ranked set samples

Moniri, Seyed Jalaleddin (2021) Semi-parametric mixture models with ranked set samples. Masters thesis, Memorial University of Newfoundland.

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Simple random sampling (SRS) is the common method in data collection. In many applications, measuring the variable of interest is costly, but ranking the units can be done easily. In these situations, one can use rank set sampling (RSS) to get more representative samples from the population. This thesis investigates the estimation of the semi-parametric finite mixture models (FMMs) with RSS. We develop a semiparametric version of the Expectation-Maximization (EM) algorithm to obtain the maximum likelihood (ML) estimate of the population with RSS data. We then propose the ML estimation of FMM with RSS data in a semi-parametric framework. Our numerical studies show that the proposed EM algorithm estimates more efficiently the FMM. The proposed methods are finally applied to analyze the bone mineral data.

Item Type: Thesis (Masters)
Item ID: 15224
Additional Information: Includes bibliographical references (pages 88-94).
Keywords: finite mixture model, ranked set sampling, semi-parametric estimation, misplacement probability model, EM algorithm, bone mineral data
Department(s): Science, Faculty of > Mathematics and Statistics
Date: August 2021
Date Type: Submission
Digital Object Identifier (DOI):
Library of Congress Subject Heading: Expectation-maximization algorithms; Sampling (Statistics); Mathematical statistics; Finite model theory; Economics, Mathematical; RSS feeds.

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