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A restricted gamma ridge regression estimator combining the gamma ridge regression and the restricted maximum likelihood methods of estimation

Qasim, Muhammad LU ; Akram, Muhammad Nauman ; Amin, Muhammad and Månsson, Kristofer (2022) In Journal of Statistical Computation and Simulation 92(8). p.1696-1713
Abstract

In this article, we propose a restricted gamma ridge regression estimator (RGRRE) by combining the gamma ridge regression (GRR) and restricted maximum likelihood estimator (RMLE) to combat multicollinearity problem for estimating the parameter (Formula presented.) in the gamma regression model. The properties of the new estimator are discussed, and its superiority over the GRR, RMLE and traditional maximum likelihood estimator is theoretically analysed under different conditions. We also suggest some estimating methods to find the optimal value of the shrinkage parameter. A Monte Carlo simulation study is conducted to judge the performance of the proposed estimator. Finally, an empirical application is analysed to show the benefit of... (More)

In this article, we propose a restricted gamma ridge regression estimator (RGRRE) by combining the gamma ridge regression (GRR) and restricted maximum likelihood estimator (RMLE) to combat multicollinearity problem for estimating the parameter (Formula presented.) in the gamma regression model. The properties of the new estimator are discussed, and its superiority over the GRR, RMLE and traditional maximum likelihood estimator is theoretically analysed under different conditions. We also suggest some estimating methods to find the optimal value of the shrinkage parameter. A Monte Carlo simulation study is conducted to judge the performance of the proposed estimator. Finally, an empirical application is analysed to show the benefit of RGRRE over the existing estimators.

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author
; ; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Gamma regression model, maximum likelihood estimator, mean squared error, multicollinearity, restricted gamma ridge regression estimator
in
Journal of Statistical Computation and Simulation
volume
92
issue
8
pages
18 pages
publisher
Taylor & Francis
external identifiers
  • scopus:85120084796
ISSN
0094-9655
DOI
10.1080/00949655.2021.2005063
language
English
LU publication?
no
additional info
Publisher Copyright: © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
id
20271db6-539e-4637-b1b5-bd844f19057e
date added to LUP
2025-03-24 17:23:15
date last changed
2025-04-04 15:18:45
@article{20271db6-539e-4637-b1b5-bd844f19057e,
  abstract     = {{<p>In this article, we propose a restricted gamma ridge regression estimator (RGRRE) by combining the gamma ridge regression (GRR) and restricted maximum likelihood estimator (RMLE) to combat multicollinearity problem for estimating the parameter (Formula presented.) in the gamma regression model. The properties of the new estimator are discussed, and its superiority over the GRR, RMLE and traditional maximum likelihood estimator is theoretically analysed under different conditions. We also suggest some estimating methods to find the optimal value of the shrinkage parameter. A Monte Carlo simulation study is conducted to judge the performance of the proposed estimator. Finally, an empirical application is analysed to show the benefit of RGRRE over the existing estimators.</p>}},
  author       = {{Qasim, Muhammad and Akram, Muhammad Nauman and Amin, Muhammad and Månsson, Kristofer}},
  issn         = {{0094-9655}},
  keywords     = {{Gamma regression model; maximum likelihood estimator; mean squared error; multicollinearity; restricted gamma ridge regression estimator}},
  language     = {{eng}},
  number       = {{8}},
  pages        = {{1696--1713}},
  publisher    = {{Taylor & Francis}},
  series       = {{Journal of Statistical Computation and Simulation}},
  title        = {{A restricted gamma ridge regression estimator combining the gamma ridge regression and the restricted maximum likelihood methods of estimation}},
  url          = {{http://dx.doi.org/10.1080/00949655.2021.2005063}},
  doi          = {{10.1080/00949655.2021.2005063}},
  volume       = {{92}},
  year         = {{2022}},
}