Performance of A New Bias Corrected Estimator in Beta Regression Model: A Monte Carlo Study

Yasin Asar

Article Details

Performance of A New Bias Corrected Estimator in Beta Regression Model: A Monte Carlo Study

Authors:

Yasin Asar

Publisher:

Necmettin Erbakan University

Main Topics:

Hesaplamalı İstatistik

Journal:

Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi Vol.5 Issue 2, pp.75-87

Document Type:

Article

Publication Date:

12/2023

Document Language:

English

Document ID (Org No.):

2001304652

pISSN:

2667-7989

Keywords:

Abstract:

 The primary approach for obtaining regression parameters in the beta regression model is the utilization of the maximum likelihood estimation technique. However, it is acknowledged that multicollinearity has a detrimental effect on the variance of the maximum likelihood estimator in the beta regression model, namely, the variance of the maximum likelihood estimator is inflated. To address this issue, a novel bias-adjusted estimator is introduced to tackle the problem of multicollinearity. The effectiveness of these new estimator is assessed through a numerical investigation using a Monte Carlo simulation experiment. The results indicate that the proposed estimators yield substantial improvements compared to other competing estimators in terms of both the mean squared error and squared bias values.

 The primary approach for obtaining regression parameters in the beta regression model is the utilization of the maximum likelihood estimation technique. However, it is acknowledged that multicollinearity has a detrimental effect on the variance of the maximum likelihood estimator in the beta regression model, namely, the variance of the maximum likelihood estimator is inflated. To address this issue, a novel bias-adjusted estimator is introduced to tackle the problem of multicollinearity. The effectiveness of these new estimator is assessed through a numerical investigation using a Monte Carlo simulation experiment. The results indicate that the proposed estimators yield substantial improvements compared to other competing estimators in terms of both the mean squared error and squared bias values.

 The primary approach for obtaining regression parameters in the beta regression model is the utilization of the maximum likelihood estimation technique. However, it is acknowledged that multicollinearity has a detrimental effect on the variance of the maximum likelihood estimator in the beta regression model, namely, the variance of the maximum likelihood estimator is inflated. To address this issue, a novel bias-adjusted estimator is introduced to tackle the problem of multicollinearity. The effectiveness of these new estimator is assessed through a numerical investigation using a Monte Carlo simulation experiment. The results indicate that the proposed estimators yield substantial improvements compared to other competing estimators in terms of both the mean squared error and squared bias values.

 Beta regresyon modelinde regresyon parametrelerini elde etmek için birincil yaklaşım, maksimum olabilirlik tahmin tekniğinin kullanılmasıdır. Bununla birlikte, beta regresyon modelinde çoklu bağlantının maksimum olabilirlik tahmin edicisinin varyansı üzerinde negatif bir etkiye sahip olduğu, yani maksimum olabilirlik tahmin edicisinin varyansının şişirildiği kabul edilmektedir. Bu konuyu ele almak için, çoklu bağlantı sorununu çözmek için yeni bir yanı düzeltilmiş tahmin edici tanıtılmıştır. Bu yeni tahmin edicinin etkinliği, bir Monte Carlo simülasyon deneyi kullanılarak sayısal bir araştırma yoluyla değerlendirilmiştir. Sonuçlar, önerilen tahmin edicinin diğer rakip tahmin edicilere kıyasla hem hata kareler ortalaması hem de karesel yan değerleri bakımından önemli iyileştirmeler sağladığını göstermektedir.

Article Details

Performance of A New Bias Corrected Estimator in Beta Regression Model: A Monte Carlo Study

Authors:

Yasin Asar

Publisher:

Necmettin Erbakan University

Main Topics:

Hesaplamalı İstatistik

Journal:

Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi Vol.5 Issue 2, pp.75-87

Document Type:

Article

Publication Date:

12/2023

Document Language:

English

Document ID (Org No.):

2001304652

pISSN:

2667-7989

Beta regression

Multicollinearity

Bias-adjeusted estimator

Maximum likelihoodBeta regresyon

Çoklu bağlantı

Yanı düzeltilmiş tahmin edici

Maksimum olabilirlik

Issues

Article Details

Authors: Yasin Asar

Publisher: Necmettin Erbakan University

Main Topics: Hesaplamalı İstatistik

Journal: Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi Vol.5 Issue 2, pp.75-87

Document Type: Article

Publication Date: 12/2023

Document Language: English

Document ID (Org No.): 2001304652

pISSN: 2667-7989

Issues

Authors:

Yasin Asar

Publisher:

Necmettin Erbakan University

Main Topics:

Hesaplamalı İstatistik

Journal:

Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi Vol.5 Issue 2, pp.75-87

Document Type:

Article

Publication Date:

12/2023

Document Language:

English

Document ID (Org No.):

2001304652

pISSN:

2667-7989