Title : ( Robust biased estimators for Poisson regression model: Simulation and applications )
Authors: Lukman A F , Mohammad Arashi , Prokaj Vilmos ,
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Abstract
The method of maximum likelihood flops when there is linear dependency (multi-collinearity) and outlier in the generalized linear models. In this study, we combined the ridge estimator with the transformed M-estimator (MT) and the conditionally unbiased bounded influence estimator (CE). The two new estimators are called the robust MT estimator and Robust-CE. A Monte Carlo study revealed that the proposed estimators dominateforthegeneralizedlinearmodelswithPoissonresponseandloglinkfunction. The real-life application results support the simulation outcome.
Keywords
, conditionally unbiased bounded influence estimator, multicollinearity, outlier, ridge estimator, robust CE, robust MT, transformed M-estimator
@article{paperid:1093067,
author = {لوکمان، ا ف and Arashi, Mohammad and پروکاش، ویلماس},
title = {Robust biased estimators for Poisson regression model: Simulation and applications},
journal = {Concurrency and Computation: Practice and Experience},
year = {2023},
volume = {35},
number = {7},
month = {January},
issn = {1532-0626},
keywords = {conditionally unbiased bounded influence estimator; multicollinearity; outlier; ridge estimator; robust CE; robust MT; transformed M-estimator},
}
%0 Journal Article
%T Robust biased estimators for Poisson regression model: Simulation and applications
%A لوکمان، ا ف
%A Arashi, Mohammad
%A پروکاش، ویلماس
%J Concurrency and Computation: Practice and Experience
%@ 1532-0626
%D 2023
