An Empirical Study of Generalized Linear Model for Count Data

2015 ◽  
Vol 04 (05) ◽  
Author(s):  
Muritala Abdulkabir Udokang Anietie ◽  
Edem Raji Surajudeen ◽  
Tunde Bello Latifat Kemi
Keyword(s):  
Empirical Study ◽  
Linear Model ◽  
Count Data ◽  
Generalized Linear Model

scholarly journals Risk Measuring Model on Public Liability Fire and Empirical Study in China

2014 ◽  
Vol 9 (1) ◽  
pp. 35-41
Author(s):  
Guo-Xue Gu ◽  
◽  
Shang-Mei Zhao
Keyword(s):  
Risk Factors ◽  
Empirical Study ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Gaussian Kernel ◽  
Favorable Effect ◽  
Public Places ◽  
Fire Insurance ◽  
In Fire

Public fire insurance has recently appeared in China. The basis for calculating the premium is the accurate measurement of Publicliability risk in fire. The generalized linear model (GLM) is widely used for measuring this risk in practice, but the GLM often cannot be satisfied, especially in fat-tailed distribution. A nonparametric Gaussian kernel linear model used to improve the GLM is applied to measure publicliability risk in fire, yielding a favorable effect. Results show three major risk factors that were measured precisely – the nature of the industry, the scale of public places and the level of fire precaution.

scholarly journals Non-Convex Penalized Estimation of Count Data Responses via Generalized Linear Model (GLM)

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Rasaki Olawale Olanrewaju ◽  
Johnson Funminiyi Ojo
Keyword(s):  
Sample Size ◽  
Linear Model ◽  
Count Data ◽  
Generalized Linear Model ◽  
Negative Binomial ◽  
Estimation Procedure ◽  
Absolute Deviation ◽  
Penalized Estimation ◽  
Binomial Regression ◽  
Minimax Concave Penalty

This study provided a non-convex penalized estimation procedure via Smoothed Clipped Absolute Deviation (SCAD) and Minimax Concave Penalty (MCP) for count data responses to checkmate the problem of covariates exceeding the sample size . The Generalized Linear Model (GLM) approach was adopted in obtaining the penalized functions needed by the MCP and SCAD non-convex penalizations of Binomial, Poisson and Negative-Binomial related count responses regression. A case study of the colorectal cancer with six (6) covariates against sample size of five (5) was subjected to the non-convex penalized estimation of the three distributions. It was revealed that the non-convex penalization of Binomial regression via MCP and SCAD best explained four un-penalized covariates needed in determining whether surgical or therapy ideal for treating the turmoil.

The Multilevel Generalized Linear Model for Categorical and Count Data

2017 ◽  
pp. 130-147
Author(s):  
Joop J. Hox ◽  
Mirjam Moerbeek ◽  
Rens van de Schoot
Keyword(s):  
Linear Model ◽  
Count Data ◽  
Generalized Linear Model

scholarly journals Comparing Generalized Linear Model of Count Data: Application towards Pteridophyta species

2018 ◽  
Vol 7 (3.28) ◽  
pp. 58
Author(s):  
Mohd Asrul Affendi Abdullah ◽  
Siti Afiqah Muhamad Jamil ◽  
Faridah Kormin ◽  
Mustafa Mamat
Keyword(s):  
Public Health ◽  
Linear Model ◽  
Count Data ◽  
Generalized Linear Model ◽  
Medical Studies ◽  
Data Application ◽  
Species Analysis ◽  
And Control ◽  
Public Health Epidemiology ◽  
Diversity Metrics

Pteridophyta is known as “paku-pakis” in Malay and it is one the flora species that exists in ecological system. Besides, Pteridophyta is one of the species that need to be preserved. Either flora or fauna, both are very important to preserve the ecosystem and control the pollution. In order to observe the species, the fundamental unit of all diversity metrics is a count of specific individuals. In some consequences, the uncorrected counts of observed species often used in measuring the diversity which ignore detection together and established methods to be used to account for missed species. Analysis of count data is widely used in engineering, public health, epidemiology, medical studies, ecology and many research of interest. Rarity increases the number of locations with zero detection in excess of those expected under simple models of abundance. The aims of this study are to compare the Generalized Linear Model (GLiM) in the application of Pteridophyta species of count data.  

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