In this paper, we explore the negative binomial regression model from the family of generalized linear models for the prediction of the future infection pattern of COVID-19 in Nigeria. We approached the predict
semiparametric negative binomial regressionoverdispersionThe negative binomial (NB) is frequently used to model overdispersed Poisson count data. To study the effect of a continuous covariate of interest in an NB model, a flexible procedure is used to model the covariate effect by fixed-knot cubic ...
This paper is concerned with introducing a family of multivariate mixed Negative Binomial regression models in the context of a posteriori ratemaking. The multivariate mixed Negative Binomial regression model can be considered as a candidate model for capturing overdispersion and positive dependencies in mu...
Xue, DixiDeddens, James A.Marcel Dekker, Inc.Communications in StatisticsXue D, Deddens JA (1992) Overdispersed negative binomial regression models. Communications in Statistics-Theory and Methods, 21, 2215-26.Xue, D, Deddens, J: Overdispersed negative binomial models. Commun. Stat. A-Theor....
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We propose analyzing the count data directly using regression models with the log odds link function. With this approach, the parameter estimates in the model have the exact same interpretation as in a logistic regression of the dichotomized data, yielding comparable estimates of the OR. We prove...
regression models. In the HHG model, the negative binomial mass function can be written as ( ) ( ) ( ) it i it i i it it it it i it it y y y y f λ θθ θ λ λ θλ + + + ΓΓ +Γ = 1 1 1 1 ) , | ( (4) where Γ is the gamma function. The parameter...
The traditional negative binomial regression model (NB2) was implemented by maximum likelihood estimation without much difficulty, thanks to the maximization command and especially to the automatic computation of the standard errors via the Hessian. Other negative binomial models, such as the zero-truncat...
Models:Poisson(assumesE(Y)=V(Y))NegativeBinomial(AllowsforV(Y)>E(Y))PoissonRegression •RandomComponent:PoissonDistributionfor#ofLeadChanges •SystematicComponent:LinearfunctionwithPredictors:Laps,Drivers,Trklength •LinkFunction:log:g(m)=ln(m)Mass Function:PY y | X1,X2,...
Commonly used statistical models include count data models such as the Poisson regression model, suited for modeling dependent variables that can only take non-negative integer values (Ye et al., 2018); negative binomial regression models accommodate modeling crash data with overdispersion (Khattak et...