Almost unbiased Liu-type estimators in gamma regression model

2022 ◽  
Vol 403 ◽  
pp. 113819
Author(s):  
Yasin Asar ◽  
Merve Korkmaz
Keyword(s):  
Regression Model ◽  
Almost Unbiased

scholarly journals Preliminary test almost unbiased ridge estimator in a linear regression model with multivariate Student-t errors

2020 ◽  
Vol 15 (1) ◽  
pp. 27-43
Author(s):  
Jianwen Xu ◽  
Hu Yang
Keyword(s):  
Regression Model ◽  
Sufficient Conditions ◽  
Preliminary Test ◽  
Multiple Regression Model ◽  
Regression Coefficients ◽  
Lagrangian Multiplier ◽  
Ridge Estimator ◽  
Lm Tests ◽  
Theoretical Results ◽  
Almost Unbiased

In this paper, the preliminary test almost unbiased ridge estimators of the regression coefficients based on the conflicting Wald (W), Likelihood ratio (LR) and Lagrangian multiplier (LM) tests in a multiple regression model with multivariate Student-t errors are introduced when it is suspected that the regression coefficients may be restricted to a subspace. The bias and quadratic risks of the proposed estimators are derived and compared. Sufficient conditions on the departure parameter ∆ and the ridge parameter k are derived for the proposed estimators to be superior to the almost unbiased ridge estimator, restricted almost unbiased ridge estimator and preliminary test estimator. Furthermore, some graphical results are provided to illustrate theoretical results.

Almost unbiased ridge estimator in the gamma regression model

2020 ◽  
pp. 1-21 ◽  
Author(s):  
Muhammad Amin ◽  
Muhammad Qasim ◽  
Ahad Yasin ◽  
Muhammad Amanullah
Keyword(s):  
Regression Model ◽  
Ridge Estimator ◽  
Almost Unbiased

scholarly journals Stochastic Restricted Biased Estimators in Misspecified Regression Model with Incomplete Prior Information

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Manickavasagar Kayanan ◽  
Pushpakanthie Wijekoon
Keyword(s):  
Regression Model ◽  
Prior Information ◽  
Ridge Estimator ◽  
Monte Carlo Simulation Study ◽  
Error Matrix ◽  
Liu Estimator ◽  
Explanatory Variables ◽  
Biased Estimators ◽  
Incomplete Prior Information ◽  
Almost Unbiased

The analysis of misspecification was extended to the recently introduced stochastic restricted biased estimators when multicollinearity exists among the explanatory variables. The Stochastic Restricted Ridge Estimator (SRRE), Stochastic Restricted Almost Unbiased Ridge Estimator (SRAURE), Stochastic Restricted Liu Estimator (SRLE), Stochastic Restricted Almost Unbiased Liu Estimator (SRAULE), Stochastic Restricted Principal Component Regression Estimator (SRPCRE), Stochastic Restricted r-k (SRrk) class estimator, and Stochastic Restricted r-d (SRrd) class estimator were examined in the misspecified regression model due to missing relevant explanatory variables when incomplete prior information of the regression coefficients is available. Further, the superiority conditions between estimators and their respective predictors were obtained in the mean square error matrix (MSEM) sense. Finally, a numerical example and a Monte Carlo simulation study were used to illustrate the theoretical findings.

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