Parameter Evaluation for a Statistical Mechanical Model for Binary Choice with Social Interaction

In this paper we use a statistical mechanical model as a paradigm for educational choices when the reference population is partitioned according to the socioeconomic attributes of gender and residence. We study how educational attainment is influenced by socioeconomic attributes of gender and reside...

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Main Authors: Alex Akwasi Opoku, Godwin Osabutey, Charles Kwofie
Format: Article
Language:English
Published: Hindawi Limited 2019-01-01
Series:Journal of Probability and Statistics
Online Access:http://dx.doi.org/10.1155/2019/3435626
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spelling doaj-a100c04061154e73a34feb7af12c60e02020-11-25T02:21:25ZengHindawi LimitedJournal of Probability and Statistics1687-952X1687-95382019-01-01201910.1155/2019/34356263435626Parameter Evaluation for a Statistical Mechanical Model for Binary Choice with Social InteractionAlex Akwasi Opoku0Godwin Osabutey1Charles Kwofie2Mathematics and Statistics Department, University of Energy and Natural Resources, P. O. Box 214, Sunyani, GhanaMathematics and Statistics Department, University of Energy and Natural Resources, P. O. Box 214, Sunyani, GhanaMathematics and Statistics Department, University of Energy and Natural Resources, P. O. Box 214, Sunyani, GhanaIn this paper we use a statistical mechanical model as a paradigm for educational choices when the reference population is partitioned according to the socioeconomic attributes of gender and residence. We study how educational attainment is influenced by socioeconomic attributes of gender and residence for five selected developing countries. The model has a social and a private incentive part with coefficients measuring the influence individuals have on each other and the external influence on individuals, respectively. The methods of partial least squares and the ordinary least squares are, respectively, used to estimate the parameters of the interacting and the noninteracting models. This work differs from the previous work that motivated this work in the following sense: (a) the reference population is divided into subgroups with unequal subgroup sizes, (b) the proportion of individuals in each of the subgroups may depend on the population size N, and (c) the method of partial least squares is used for estimating the parameters of the model with social interaction as opposed to the least squares method used in the earlier work.http://dx.doi.org/10.1155/2019/3435626
collection DOAJ
language English
format Article
sources DOAJ
author Alex Akwasi Opoku
Godwin Osabutey
Charles Kwofie
spellingShingle Alex Akwasi Opoku
Godwin Osabutey
Charles Kwofie
Parameter Evaluation for a Statistical Mechanical Model for Binary Choice with Social Interaction
Journal of Probability and Statistics
author_facet Alex Akwasi Opoku
Godwin Osabutey
Charles Kwofie
author_sort Alex Akwasi Opoku
title Parameter Evaluation for a Statistical Mechanical Model for Binary Choice with Social Interaction
title_short Parameter Evaluation for a Statistical Mechanical Model for Binary Choice with Social Interaction
title_full Parameter Evaluation for a Statistical Mechanical Model for Binary Choice with Social Interaction
title_fullStr Parameter Evaluation for a Statistical Mechanical Model for Binary Choice with Social Interaction
title_full_unstemmed Parameter Evaluation for a Statistical Mechanical Model for Binary Choice with Social Interaction
title_sort parameter evaluation for a statistical mechanical model for binary choice with social interaction
publisher Hindawi Limited
series Journal of Probability and Statistics
issn 1687-952X
1687-9538
publishDate 2019-01-01
description In this paper we use a statistical mechanical model as a paradigm for educational choices when the reference population is partitioned according to the socioeconomic attributes of gender and residence. We study how educational attainment is influenced by socioeconomic attributes of gender and residence for five selected developing countries. The model has a social and a private incentive part with coefficients measuring the influence individuals have on each other and the external influence on individuals, respectively. The methods of partial least squares and the ordinary least squares are, respectively, used to estimate the parameters of the interacting and the noninteracting models. This work differs from the previous work that motivated this work in the following sense: (a) the reference population is divided into subgroups with unequal subgroup sizes, (b) the proportion of individuals in each of the subgroups may depend on the population size N, and (c) the method of partial least squares is used for estimating the parameters of the model with social interaction as opposed to the least squares method used in the earlier work.
url http://dx.doi.org/10.1155/2019/3435626
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