A semiparametric negative binomial generalized linear model for modeling over-dispersed count data with a heavy tail: Characteristics and applications to crash data

2016 ◽  
Vol 91 ◽  
pp. 10-18 ◽  
Author(s):  
Mohammadali Shirazi ◽  
Dominique Lord ◽  
Soma Sekhar Dhavala ◽  
Srinivas Reddy Geedipally
Keyword(s):  
Linear Model ◽  
Count Data ◽  
Generalized Linear Model ◽  
Negative Binomial ◽  
Heavy Tail ◽  
Crash Data

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 negative binomial-Lindley generalized linear model: Characteristics and application using crash data

2012 ◽  
Vol 45 ◽  
pp. 258-265 ◽  
Author(s):  
Srinivas Reddy Geedipally ◽  
Dominique Lord ◽  
Soma Sekhar Dhavala
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Negative Binomial ◽  
Crash Data

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 Community Mobility and COVID-19 Dynamics in Jakarta, Indonesia

2021 ◽  
Author(s):  
Ratih Oktri Nanda ◽  
Aldilas Achmad Nursetyo ◽  
Aditya Lia Ramadona ◽  
Muhammad Ali Imron ◽  
Anis Fuad ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Negative Binomial ◽  
Human Mobility ◽  
Management Strategies ◽  
Principal Component ◽  
Outbreak Management ◽  
Community Mobility

Background Human mobility could act as a vector to facilitate the spread of infectious diseases. In response to the COVID-19 pandemic, Google Community Mobility Reports (CMR) provide the necessary data to explore community mobility further. Therefore, we aimed to examine the relationship between community mobility on COVID-19 dynamics in Jakarta, Indonesia. Methods We utilized the mobility data from Google from February 15 to December 31, 2020. We explored several statistical models to estimate the COVID-19 dynamics in Jakarta. Model 1 was a Poisson Regression Generalized Linear Model (GLM), Model 2 was a Negative Binomial Regression Generalized Linear Model (GLM), and Model 3 was a Multiple Linear Regression (MLR). Results We found that Multiple Linear Regression (MLR) with some adjustments using Principal Component Analysis (PCA) was the best fit model. It explained 52% of COVID-19 cases in Jakarta (R-Square: 0.52, p<0.05). All mobility variables were significant predictors of COVID-19 cases (p<0.05). More precisely, about 1% change in grocery and pharmacy would contribute to a 4.12% increase of the COVID-19 cases in Jakarta. Retails and recreations, workplaces, transit stations, and parks would result in 3.11%, 2.56%, 2.26%, and 1.93% of more COVID-19 cases, respectively. Conclusion Our study indicates that increased mobility contributes to increased COVID-19 cases. This finding will be beneficial to assist policymakers to have better outbreak management strategies, to anticipate increased COVID-19 cases in the future at certain public places and during seasonal events such as annual religious holidays or other long holidays in particular.

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 Correlational study of air pollution-related diseases (asthma, conjunctivitis, URTI and dengue) in Johor Bahru, Malaysia

2017 ◽  
Vol 13 (4-1) ◽  
pp. 354-361 ◽  
Author(s):  
Aaishah Radziah Jamaludin ◽  
Fadhilah Yusof ◽  
Rahmah Mohd Lokoman ◽  
Zainura Zainoon Noor ◽  
Noreliza Alias ◽  
...  
Keyword(s):  
Air Pollution ◽  
Linear Model ◽  
Sulphur Dioxide ◽  
Generalized Linear Model ◽  
Negative Binomial ◽  
Influential Factors ◽  
Motor Vehicles ◽  
Binomial Model ◽  
Climate Variables ◽  
Mobile Sources

Four pollution related diseases, namely asthma, conjunctivitis, URTI and dengue will be studied in terms of their trend, behaviour and association with influential factors such as air pollution and climate variables. Two methods were chosen; Poisson Generalized Linear Model and Negative Binomial Model. These methods were used to determine the association between the diseases and their influential factors. This study shows that Sulphur Dioxide (SO2) is the most abundant source that contributes to the diseases. Therefore, the local authorities such as the Department of Environment need to reinforce the law in planning and monitoring the SO2 sources which are produced from fuel combustion in mobile sources and motor vehicles. 

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