Bell Regresyon Modelinde Liu tipi Tahmin Edici

Işilar, Melike; Bulut, Y. Murat

doi:10.19113/sdufenbed.1119370

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…In particular, the RE and LE estimators have been proposed as robust ridge and robust Liu estimators, along with many robust estimators. Some of these studies are Silvapulle, Arslan and Billor, Arslan and Billor, Midi and Zahari, Ma and Du, Filzmoser and Kurnaz, Işılar (Arslan & Billor, 1996, 2000; Filzmoser & Kurnaz, 2018; Işılar, 2020; Ma & Du, 2014; Midi & Zahari, 2007; Silvapulle, 1991).…”

Section: Introductionmentioning

confidence: 99%

Robust Liu‐type estimator based on GM estimator

Işilar

Bulut

2023

Statistica Neerlandica

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Ordinary Least Squares Estimator (OLSE) is widely used to estimate parameters in regression analysis. In practice, the assumptions of regression analysis are often not met. The most common problems that break these assumptions are outliers and multicollinearity problems. As a result of these problems, OLSE loses efficiency. Therefore, alternative estimators to OLSE have been proposed to solve these problems. Robust estimators are often used to solve the outlier problem, and biased estimators are often used to solve the multicollinearity problem. These problems do not always occur individually in the real‐world dataset. Therefore, robust biased estimators are proposed for simultaneous solutions to these problems. The aim of this study is to propose Liu‐type Generalized M Estimator as an alternative to the robust biased estimators available in the literature to obtain more efficient results. This estimator gives effective results in the case of outlier and multicollinearity in both dependent and independent variables. The proposed estimator is theoretically compared with other estimators available in the literature. In addition, Monte Carlo simulation and real dataset example are performed to compare the performance of the estimator with existing estimators.

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