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A new Poisson Liu Regression Estimator: method and application.

Authors :
Qasim, Muhammad
Kibria, B. M. G.
Månsson, Kristofer
Sjölander, Pär
Source :
Journal of Applied Statistics. Sep2020, Vol. 47 Issue 12, p2258-2271. 14p. 8 Charts, 1 Graph.
Publication Year :
2020

Abstract

This paper considers the estimation of parameters for the Poisson regression model in the presence of high, but imperfect multicollinearity. To mitigate this problem, we suggest using the Poisson Liu Regression Estimator (PLRE) and propose some new approaches to estimate this shrinkage parameter. The small sample statistical properties of these estimators are systematically scrutinized using Monte Carlo simulations. To evaluate the performance of these estimators, we assess the Mean Square Errors (MSE) and the Mean Absolute Percentage Errors (MAPE). The simulation results clearly illustrate the benefit of the methods of estimating these types of shrinkage parameters in finite samples. Finally, we illustrate the empirical relevance of our newly proposed methods using an empirically relevant application. Thus, in summary, via simulations of empirically relevant parameter values, and by a standard empirical application, it is clearly demonstrated that our technique exhibits more precise estimators, compared to traditional techniques – at least when multicollinearity exist among the regressors. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02664763
Volume :
47
Issue :
12
Database :
Academic Search Index
Journal :
Journal of Applied Statistics
Publication Type :
Academic Journal
Accession number :
145323441
Full Text :
https://doi.org/10.1080/02664763.2019.1707485