Results 191 to 200 of about 543,870 (241)

A new Stein estimator for the zero‐inflated negative binomial regression model

Concurrency and Computation, 2022
The Zero‐inflated negative binomial (ZINB) regression models are mainly applied for count data that shows over‐dispersion and extra zeros. Multicollinearity is considered to be a significant problem in the estimation of parameters in the ZINB regression ...
M. Akram, M. R. Abonazel, Muhammad Amin, B. G. Kibria, Nimrah Afzal   +4 more
semanticscholar   +1 more source

Hurdle Negative Binomial Regression Model

ICSA - International Conference on Statistics and Analytics 2019, 2021
Poisson regression is a common regression model used for count data with equidispersion. However, in real data application, overdispersion often encountered, suggesting the seek for alternative model to the Poisson regression. In overdispersion data due to excess zeros and additional overdispersion in positive values, one of alternative model that can ...
Ayu Andika, Sarini Abdullah, Siti Nurrohmah   +2 more
openaire   +1 more source

Negative binomial regression models and estimation methods

Highway Safety Analytics and Modeling, 2021
openaire   +2 more sources

Some almost unbiased ridge regression estimators for the zero-inflated negative binomial regression model

Periodicals of Engineering and Natural Sciences (PEN), 2020
Zero-inflated negative binomial regression (ZINB) models are commonly used for count data that show overdispersion and extra zeros. The correlation among variables of the count data leads to the presence of a multicollinearity problem.
Y. Al-Taweel, Z. Algamal
semanticscholar   +1 more source

Macro-level collision prediction using geographically weighted negative binomial regression

Journal of Transportation Safety & Security, 2020
We developed and tested geographically weighted Poisson regression and geographically weighted negative binomial regression models using five year’s collisions, traffic, socio-demographic, road inventory, and land use data for Regina, Saskatchewan ...
S. Oluwajana, Peter Y. Park, Thais Cavalho   +2 more
semanticscholar   +1 more source

Spatial zero-inflated negative binomial regression models: Application for estimating frequencies of rear-end crashes on Thai highways

Journal of Transportation Safety & Security, 2020
Objective: Rear-end crashes are a type of road traffic accident that occurs frequently. Currently, the application of advanced statistical models to predict the frequency of accident numbers has increased because such models enable accuracy in ...
Thanapong Champahom, Sajjakaj Jomnonkwao, Ampol Karoonsoontawong, V. Ratanavaraha   +3 more
semanticscholar   +1 more source

Overdispersed negative binomial regression models

Communications in Statistics - Theory and Methods, 1992
Overdispersion is a common phenomenon in actual data sets. It is important to have methods of dealing with extra variation in regression situations. This article develops tests for extra-negative binomial variation and gives some numerical methods to deal with it.
Dixi Xue, James A. Deddens
openaire   +1 more source

Poisson and Negative Binomial Regression Analysis

2020
Anusha Musunuru, David Proffitt, Reid Ewing, William H. Greene   +3 more
openaire   +2 more sources

Seemingly Unrelated Negative Binomial Regression

Oxford Bulletin of Economics and Statistics, 2000
This paper discusses the specification and estimation of seemingly unrelated multivariate count data models. A new model with negative binomial marginals is proposed. In contrast to a previous model based on the multivariate Poisson distribution, the new model allows for over‐dispersion, a phenomenon that is frequently encountered in economic count ...
openaire   +1 more source

Geographically Weighted Negative Binomial Regression—incorporating overdispersion

Statistics and Computing, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
da Silva, Alan Ricardo, Valadares Rodrigues, Thais Carvalho   +1 more
openaire   +1 more source