Statistical Analysis of Tuberculosis and HIV Cases in West Africa Using Panel Poisson and Negative Binomial Regression Models

West African ◽

World Health ◽

Negative Binomial Regression Model ◽

African Countries ◽

Tuberculosis cause of death worldwide and the leading cause from a single infectious agent, ranking above Human immunodeficiency virus (HIV) and Acquired Immune Deficiency Syndrome (AIDS). The aim of this study is to ascertain the trend of tuberculosis prevalence and the effect of HIV prevalence onl Tuberculosis case in some West African countries from 2000 to 2016 using count panel data regression models. The data used annual HIV and Tuberculosis cases spanning from 2000 to 2016 extracted from online publication of World health Organization (WHO). Panel Poisson regression model and Negative binomial regression model for fixed and random effects were used to analyzed the count data, the result revealed a positive trend in TB cases while increased in HIV cases leads to increase in TB cases in West African countries. Among the competing models used in this study, Panel Negative Binomial Regression Model with fixed effect emerged the best model with log likelihood value of -1336.554. This study recommended that Government and NGOs need more strategies to fight against HIV menace in West Africa as this will in turn reduced TB cases in West Africa.

Statistical Analysis of Climate Factors Influencing Dengue Incidences in Colombo, Sri Lanka: Poisson and Negative Binomial Regression Approach

International Journal of Scientific and Research Publications (IJSRP) ◽

10.29322/ijsrp.9.02.2019.p8616 ◽

2019 ◽

Vol 9 (2) ◽

pp. p8616 ◽

Cited By ~ 1

Author(s):

Leslie Chandrakantha

Keyword(s):

Statistical Analysis ◽

Sri Lanka ◽

Negative Binomial ◽

Climate Factors ◽

Factors Influencing ◽

Binomial Regression ◽

Regression Approach

Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models

BMC Public Health ◽

10.1186/s12889-015-1500-4 ◽

2015 ◽

Vol 15 (1) ◽

Cited By ~ 5

Author(s):

Ta-Chien Chan ◽

Yung-Chu Teng ◽

Jing-Shiang Hwang

Keyword(s):

Regression Models ◽

Negative Binomial ◽

Influenza Like Illness ◽

Pearson Residuals ◽

Managing Inflation

Crime & Delinquency ◽

10.1177/0011128716679796 ◽

2016 ◽

Vol 63 (1) ◽

pp. 77-87 ◽

Cited By ~ 5

Author(s):

William H. Fisher ◽

Stephanie W. Hartwell ◽

Xiaogang Deng

Keyword(s):

Count Data ◽

Regression Models ◽

Negative Binomial ◽

Excess Zeros ◽

Binomial Regression ◽

Negative Binomial Models ◽

Using Data ◽

Over Dispersion ◽

Binomial Models

Poisson and negative binomial regression procedures have proliferated, and now are available in virtually all statistical packages. Along with the regression procedures themselves are procedures for addressing issues related to the over-dispersion and excessive zeros commonly observed in count data. These approaches, zero-inflated Poisson and zero-inflated negative binomial models, use logit or probit models for the “excess” zeros and count regression models for the counted data. Although these models are often appropriate on statistical grounds, their interpretation may prove substantively difficult. This article explores this dilemma, using data from a study of individuals released from facilities maintained by the Massachusetts Department of Correction.

Group regularization for zero-inflated negative binomial regression models with an application to health care demand in Germany

Statistics in Medicine ◽

10.1002/sim.7804 ◽

2018 ◽

Vol 37 (20) ◽

pp. 3012-3026 ◽

Cited By ~ 2

Author(s):

Saptarshi Chatterjee ◽

Shrabanti Chowdhury ◽

Himel Mallick ◽

Prithish Banerjee ◽

Broti Garai

Keyword(s):

Health Care ◽

Regression Models ◽

Negative Binomial ◽

Health Care Demand ◽

6. Poisson and Negative Binomial Regression Models

Regression Models for Categorical, Count, and Related Variables ◽

10.1525/9780520965492-008 ◽

2019 ◽

pp. 131-158

Keyword(s):

Regression Models ◽

Negative Binomial ◽

What Do We Know About Changing Economic Activity of Firms?

Studies in Microeconomics ◽

10.1177/2321022218869790 ◽

2019 ◽

pp. 232102221886979

Author(s):

Radhika Pandey ◽

Amey Sapre ◽

Pramod Sinha

Keyword(s):

Economic Activity ◽

Regression Models ◽

Statistical Approach ◽

Negative Binomial ◽

Large Data ◽

Poisson Model ◽

Data Set ◽

Binomial Regression ◽

History Of

Identification of primary economic activity of firms is a prerequisite for compiling several macro aggregates. In this paper, we take a statistical approach to understand the extent of changes in primary economic activity of firms over time and across different industries. We use the history of economic activity of over 46,000 firms spread over 25 years from CMIE Prowess to identify the number of times firms change the nature of their business. Using the count of changes, we estimate Poisson and Negative Binomial regression models to gain predictability over changing economic activity across industry groups. We show that a Poisson model accurately characterizes the distribution of count of changes across industries and that firms with a long history are more likely to have changed their primary economic activity over the years. Findings show that classification can be a crucial problem in a large data set like the MCA21 and can even lead to distortions in value addition estimates at the industry level. JEL Classifications: D22, E00, E01

Traffic accident modeling: some statistical issues

Canadian Journal of Civil Engineering ◽

10.1139/l06-056 ◽

2006 ◽

Vol 33 (9) ◽

pp. 1115-1124 ◽

Cited By ~ 53

Author(s):

Z Sawalha ◽

T Sayed

Keyword(s):

Road Safety ◽

Regression Models ◽

Model Building ◽

Negative Binomial ◽

Prediction Models ◽

Outlier Analysis ◽

Accident Prediction ◽

Binomial Regression ◽

Statistical Issues

Accident prediction models are invaluable tools that have many applications in road safety analysis. However, there are certain statistical issues related to accident modeling that either deserve further attention or have not been dealt with adequately in the road safety literature. This paper discusses and illustrates how to deal with two statistical issues related to modeling accidents using Poisson and negative binomial regression. The first issue is that of model building or deciding which explanatory variables to include in an accident prediction model. The study differentiates between applications for which it is advisable to avoid model over-fitting and other applications for which it is desirable to fit the model to the data as closely as possible. It then suggests procedures for developing parsimonious models, i.e., models that are not over-fitted, and best-fit models. The second issue discussed in the paper is that of outlier analysis. The study suggests a procedure for the identification and exclusion of extremely influential outliers from the development of Poisson and negative binomial regression models. The procedures suggested for model building and conducting outlier analysis are more straightforward to apply in the case of Poisson regression models because of an added complexity presented by the shape parameter of the negative binomial distribution. The paper, therefore, presents flowcharts detailing the application of the procedures when modeling is carried out using negative binomial regression. The described procedures are then applied in the development of negative binomial accident prediction models for the urban arterials of the cities of Vancouver and Richmond located in the province of British Columbia, Canada. Key words: accident prediction models, overfitting, parsimony, outlier analysis, Poisson regression, negative binomial regression.