Pearson residual and efficiency of parameter estimates in generalized linear model

2011 ◽  
Vol 141 (2) ◽  
pp. 1014-1020
Author(s):  
Jing Xu ◽  
Michael LaValley
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Parameter Estimates ◽  
Pearson Residual

Mixed Generalized Linear Model for Estimating Household Trip Production

2000 ◽  
Vol 1718 (1) ◽  
pp. 61-67 ◽  
Author(s):  
Chang-Jen Lan ◽  
Patricia S. Hu
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Negative Binomial ◽  
Empirical Distribution ◽  
Distribution Functions ◽  
Model Specification ◽  
Parameter Estimates ◽  
Modeling Framework ◽  
Personal Transportation ◽  
Trip Production

An innovative modeling framework to estimate household trip rates using 1995 Nationwide Personal Transportation Survey data is presented. A generalized linear model with a mixture of negative binomial probability distribution functions was developed on the basis of characteristics observed from the empirical distribution of household daily trips. This model provides a more flexible framework and a better model specification for analyzing household-specific trip production behavior. Compared with traditional least squares-based regression models, the parameter estimates from the proposed model are more efficient. Although the mean accuracies from the two modeling approaches are comparable, the mixed generalized linear model is more robust in identifying outliers due to its unsymmetric prediction bounds derived from more correct model specification.

scholarly journals MODEL STOKASTIK DENGAN PENDEKATAN GENERALIZED LINEAR MODEL UNTUK MENGESTIMASI CADANGAN KLAIM INCURRED BUT NOT REPORTED

2021 ◽  
Vol 15 (4) ◽  
pp. 607-614
Author(s):  
Feby Seru ◽  
Azizah Azizah ◽  
Agung Dwi Saputro
Keyword(s):  
Stochastic Model ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Parameter Estimates ◽  
Reserve Estimation ◽  
Insurance Business ◽  
Parameter Values

One of the crucial things in the insurance business is determining the amount of IBNR claim reserves. The amount of IBNR's claim reserves is uncertain so it is necessary to estimate as accurately as possible. The estimation results of IBNR's claim reserves will affect the solvency and sustainability of the company. To calculate the estimated IBNR claim reserves, several approaches are used both deterministically and stochastically. This study uses a stochastic model with the GLM approach for data that is assumed to have an ODP distribution. Besides, this study also uses 2 different methods to calculate parameter estimates in the model, namely by performing parameter transformations and using the Verbeek algorithm. This study will compare the results of the IBNR claim reserve estimation obtained using these two methods in estimating the parameters in the model. The estimation results obtained indicate that the value of the IBNR claim reserves is the same. The advantage of the Verbeek algorithm is that the resulting parameter values ​​have interpretations.

scholarly journals Comparison of Generalized Linear Model and Generalized Linear Mixed Model – An Application to Low Birth Weight Data

2020 ◽  
pp. 31-37
Author(s):  
Michael Fosu Ofori ◽  
Stephen B. Twum ◽  
Jackson A. Y. Osborne
Keyword(s):  
Linear Model ◽  
Generalized Linear Models ◽  
Generalized Linear Model ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Linear Models ◽  
Generalized Linear Mixed Model ◽  
Correlated Data ◽  
Parameter Estimates ◽  
Over Dispersion

Background: Generalized Linear models are mostly fitted to data that are not correlated. However, very often data that are collected from health and epidemiological studies are correlated either as a result of the sampling methods or the randomness associated with the collection of such data. Therefore, fitting generalized linear models to such data that produce only fixed effects could lead to over dispersion in the model estimates. Objectives: The objective of this study is to fit both generalized linear and generalized linear mixed models to a correlated data and compare the results of the two models. Methods: Logistic regression is employed in fitting the generalized linear model since the dependent variable in the study is bivariate whilst the GLIMMIX model in SAS is used to fit the generalized linear mixed model. Results: The generalized linear model produces over dispersion with higher errors among the parameter estimates than the generalized linear mixed model. Conclusion: In dealing with a more correlated data, generalized linear mixed model, which can handle both fixed and random effects, is preferable to generalized linear model.

Gamma generalized linear model to investigate the effects of climate variables on the area burned by forest fire in northeast China

2015 ◽  
Vol 26 (3) ◽  
pp. 545-555 ◽  
Author(s):  
Futao Guo ◽  
Guangyu Wang ◽  
John L. Innes ◽  
Xiangqing Ma ◽  
Long Sun ◽  
...  
Keyword(s):  
Linear Model ◽  
Forest Fire ◽  
Generalized Linear Model ◽  
Northeast China ◽  
Climate Variables ◽  
Area Burned

Optimization in a multivariate generalized linear model situation

2008 ◽  
Vol 52 (10) ◽  
pp. 4625-4634 ◽  
Author(s):  
S. Mukhopadhyay ◽  
A.I. Khuri
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Model Situation
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