Adjusted quasi-maximum likelihood estimator for mixed regressive, spatial autoregressive model and its small sample bias

2015 ◽  
Vol 87 ◽  
pp. 116-135 ◽  
Author(s):  
Dalei Yu ◽  
Peng Bai ◽  
Chang Ding
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Autoregressive Model ◽  
Small Sample ◽  
Spatial Autoregressive Model ◽  
Likelihood Estimator ◽  
Sample Bias ◽  
Spatial Autoregressive ◽  
Quasi Maximum Likelihood

scholarly journals Improving the Efficiency of Robust Estimators for the Generalized Linear Model

Stats ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 88-107
Author(s):  
Alfio Marazzi
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Linear Model ◽  
Negative Binomial ◽  
Small Sample ◽  
Hospital Length Of Stay ◽  
Robust Estimator ◽  
Negative Binomial Regression Model ◽  
Likelihood Estimator ◽  
Density Power Divergence

The distance constrained maximum likelihood procedure (DCML) optimally combines a robust estimator with the maximum likelihood estimator with the purpose of improving its small sample efficiency while preserving a good robustness level. It has been published for the linear model and is now extended to the GLM. Monte Carlo experiments are used to explore the performance of this extension in the Poisson regression case. Several published robust candidates for the DCML are compared; the modified conditional maximum likelihood estimator starting with a very robust minimum density power divergence estimator is selected as the best candidate. It is shown empirically that the DCML remarkably improves its small sample efficiency without loss of robustness. An example using real hospital length of stay data fitted by the negative binomial regression model is discussed.

A Note on Small Sample Properties of Estimators in a First-Order Spatial Autoregressive Model

1982 ◽  
Vol 14 (8) ◽  
pp. 1023-1030 ◽  
Author(s):  
L Anselin
Keyword(s):  
Monte Carlo Simulation ◽  
Autoregressive Model ◽  
Ad Hoc ◽  
Simulation Experiment ◽  
Small Sample ◽  
Bayesian Estimator ◽  
Spatial Autoregressive Model ◽  
First Order ◽  
Small Sample Properties ◽  
Spatial Autoregressive

This note considers a Bayesian estimator and an ad hoc procedure for the parameters of a first-order spatial autoregressive model. The approaches are derived, and their small sample properties compared by means of a Monte Carlo simulation experiment.

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