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Generalized two-parameter estimators in the multinomial logit regression model : methods, simulation and application

Farghali, Rasha A. ; Qasim, Muhammad LU ; Kibria, B. M.Golam and Abonazel, Mohamed R. (2023) In Communications in Statistics: Simulation and Computation 52(7). p.3327-3342
Abstract

In this article, we propose generalized two-parameter (GTP) estimators and an algorithm for the estimation of shrinkage parameters to combat multicollinearity in the multinomial logit regression model. In addition, the mean squared error properties of the estimators are derived. A simulation study is conducted to investigate the performance of proposed estimators for different sample sizes, degrees of multicollinearity, and the number of explanatory variables. Swedish football league dataset is analyzed to show the benefits of the GTP estimators over the traditional maximum likelihood estimator (MLE). The empirical results of this article revealed that GTP estimators have a smaller mean squared error than the MLE and can be recommended... (More)

In this article, we propose generalized two-parameter (GTP) estimators and an algorithm for the estimation of shrinkage parameters to combat multicollinearity in the multinomial logit regression model. In addition, the mean squared error properties of the estimators are derived. A simulation study is conducted to investigate the performance of proposed estimators for different sample sizes, degrees of multicollinearity, and the number of explanatory variables. Swedish football league dataset is analyzed to show the benefits of the GTP estimators over the traditional maximum likelihood estimator (MLE). The empirical results of this article revealed that GTP estimators have a smaller mean squared error than the MLE and can be recommended for practitioners.

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Please use this url to cite or link to this publication:
author
; ; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Generalized two-parameter estimators, MSE, Multicollinearity, Multinomial logistic regression, Simulation, Swedish football league
in
Communications in Statistics: Simulation and Computation
volume
52
issue
7
pages
16 pages
publisher
Taylor & Francis
external identifiers
  • scopus:85107597614
ISSN
0361-0918
DOI
10.1080/03610918.2021.1934023
language
English
LU publication?
no
additional info
Publisher Copyright: © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.
id
91caf4d7-2e59-46fd-b0ba-776678ceccda
date added to LUP
2025-03-24 17:14:28
date last changed
2025-04-04 13:52:47
@article{91caf4d7-2e59-46fd-b0ba-776678ceccda,
  abstract     = {{<p>In this article, we propose generalized two-parameter (GTP) estimators and an algorithm for the estimation of shrinkage parameters to combat multicollinearity in the multinomial logit regression model. In addition, the mean squared error properties of the estimators are derived. A simulation study is conducted to investigate the performance of proposed estimators for different sample sizes, degrees of multicollinearity, and the number of explanatory variables. Swedish football league dataset is analyzed to show the benefits of the GTP estimators over the traditional maximum likelihood estimator (MLE). The empirical results of this article revealed that GTP estimators have a smaller mean squared error than the MLE and can be recommended for practitioners.</p>}},
  author       = {{Farghali, Rasha A. and Qasim, Muhammad and Kibria, B. M.Golam and Abonazel, Mohamed R.}},
  issn         = {{0361-0918}},
  keywords     = {{Generalized two-parameter estimators; MSE; Multicollinearity; Multinomial logistic regression; Simulation; Swedish football league}},
  language     = {{eng}},
  number       = {{7}},
  pages        = {{3327--3342}},
  publisher    = {{Taylor & Francis}},
  series       = {{Communications in Statistics: Simulation and Computation}},
  title        = {{Generalized two-parameter estimators in the multinomial logit regression model : methods, simulation and application}},
  url          = {{http://dx.doi.org/10.1080/03610918.2021.1934023}},
  doi          = {{10.1080/03610918.2021.1934023}},
  volume       = {{52}},
  year         = {{2023}},
}