Beta ridge regression estimators: simulation and application

The beta regression model is commonly used when analyzing data that come in the form of rates or percentages. However, a problem that may encounter when analyzing these kinds of data that has not been investigated for this model is the multicollinearity problem. It is well known that the maximum lik...

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Vydané v:Communications in statistics. Simulation and computation Ročník 52; číslo 9; s. 4280 - 4292
Hlavní autori: Abonazel, Mohamed R., Taha, Ibrahim M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Philadelphia Taylor & Francis 02.09.2023
Taylor & Francis Ltd
Predmet:
Beta regression model
Empirical analysis
Fisher scoring
Maximum likelihood
Maximum likelihood estimators
Mean absolute error
Mean squared error
Monte Carlo simulation
Multicollinearity
Regression models
Ridge regression
ISSN:0361-0918, 1532-4141
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Shrnutí:The beta regression model is commonly used when analyzing data that come in the form of rates or percentages. However, a problem that may encounter when analyzing these kinds of data that has not been investigated for this model is the multicollinearity problem. It is well known that the maximum likelihood (ML) method is very sensitive to high inter-correlation among the explanatory variables. Therefore, this paper proposes some ridge estimators for the beta regression model to remedy the problem of instability of the traditional ML method and increase the efficiency of estimation. The performance of ridge estimators is compared to the ML estimator through the mean squared error (MSE) and the mean absolute error (MAE) criteria by conducting a Monte-Carlo simulation study and through an empirical application. According to the simulation and application results, the proposed estimators outperform the ML estimator in terms of MSE and MAE.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2021.1960373