Robust Estimation in Generalized Linear Model Using Density Power Divergence

2019 ◽  
Vol 21 (1) ◽  
pp. 1-10
Author(s):  
Sangjin Lee ◽  
Changkon Hong
Keyword(s):  
Linear Model ◽  
Robust Estimation ◽  
Generalized Linear Model ◽  
Density Power Divergence ◽  
Power Divergence

scholarly journals Robust Estimation for Bivariate Poisson INGARCH Models

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 367
Author(s):  
Byungsoo Kim ◽  
Sangyeol Lee ◽  
Dongwon Kim
Keyword(s):  
Robust Estimation ◽  
New South ◽  
Estimation Method ◽  
Real Data ◽  
Regularity Conditions ◽  
Density Power Divergence ◽  
Asymptotically Normal ◽  
South Wales ◽  
Power Divergence ◽  
Conditional Maximum

In the integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) models, parameter estimation is conventionally based on the conditional maximum likelihood estimator (CMLE). However, because the CMLE is sensitive to outliers, we consider a robust estimation method for bivariate Poisson INGARCH models while using the minimum density power divergence estimator. We demonstrate the proposed estimator is consistent and asymptotically normal under certain regularity conditions. Monte Carlo simulations are conducted to evaluate the performance of the estimator in the presence of outliers. Finally, a real data analysis using monthly count series of crimes in New South Wales and an artificial data example are provided as an illustration.

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