Ridge-Type MML Estimator in the Linear Regression Model

Acıtaş, Şükrü; Şenoğlu, Birdal

doi:10.1007/s40995-018-0528-6

Cited by 2 publications

(1 citation statement)

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“…the LS estimators are the most efficient and the test statistics based on them are the most powerful under normality. However, nonnormality is more common in practical studies, see for example Geary (1947), Huber (1981), Islam and Tiku (2005), Senoglu (2005), Koknaroglu et al (2008), Acitas et al (2013aAcitas et al ( , 2013b, Acitas and Senoglu (2019). In the presence of nonnormality, the LS estimators may become inefficient.…”

Section: Introductionmentioning

confidence: 99%

Evaluating of beef cattle performance and profitability using robust regression analysis

Köknaroğlu

Acıtaş

Şenoğlu

2019

Harran Tarım Ve Gıda Bilimleri Dergisi

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There are several variables affecting the beef cattle performance and profitability. In the related literature, regression analysis is performed to find the contribution of the variables on profitability in different managing systems. Least squares (LS) estimation method is mostly used in regression analysis. However, it is optimal when the distribution of the error terms is normal. In this study, we revise Koknaroglu et al. (2005) study in which regression analysis is used under normality assumption. Different from the mentioned study, we use a robust estimation method called M-estimation since the error terms do not follow a normal distribution according to Shapiro-Wilk normality test. We obtain parameter estimates and their standard errors along with the coefficient of determination. It is observed that the results obtained based on M-estimation are more reliable than their LS counterparts with respect to 2 criterion.

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