- Time Series of Count Data🔍
- Accurate and Efficient Regression Modeling for Microarchitectural ...🔍
- Structured additive regression for overdispersed and zero‐in🔍
- Simulating correlated count data🔍
- Score test for ZIP mixed model🔍
- Machine learning and statistical models for analyzing multilevel ...🔍
- The multivariate mixed Negative Binomial regression model with an ...🔍
- A New Computational Algorithm for Assessing Overdispersion in ...🔍
Efficient regression modeling for correlated and overdispersed count ...
Time Series of Count Data: Modelling and Estimation - EconStor
Abstract. This paper compares various models for time series of counts which can account for dis- creetness, overdispersion and serial correlation.
Accurate and Efficient Regression Modeling for Microarchitectural ...
The model responds to predictive queries by leveraging trends and correlations in the original data set to perform statistical inference. Regression mod- eling ...
Structured additive regression for overdispersed and zero‐in
... count data regression models. Inference is fully Bayesian and is carried out by computationally efficient MCMC techniques. Simulation studies investigate ...
Simulating correlated count data - SpringerLink
However, the method requires each Y i to be overdispersed (i.e., σ2 > μ), and only low correlations are possible with this method when the variables have small ...
Score test for ZIP mixed model
Upon confirmation of overdispersion, the alternative ZINB mixed regression model may be considered for fitting the heterogeneous and correlated count data. 4.
Machine learning and statistical models for analyzing multilevel ...
The negative binomial model can be used for overdispersed count data and it can be considered as a generalization of Poisson regression since it ...
The multivariate mixed Negative Binomial regression model with an ...
efficiently model overdispersed two-dimensional ... modelling multivariate positively correlated count data when marginal overdispersion is observed.
A New Computational Algorithm for Assessing Overdispersion in ...
To address these complexities and effectively model count ... The decision to use either conventional count regression or zero-inflated modeling ...
Multiple Approaches to Analyzing Count Data in Studies of ...
regression, overdispersed Poisson regression, the negative binomial model, ordinal logistic regression, and ordinal probit regression. A simulation is ...
Bayesian Hierarchical Poisson Regression Model for Overdispersed ...
Overdispersion can be caused by positive correlation among the observations, an incorrect model, an incorrect distributional specification, or ...
exploring the importance of accounting for nonlinearity in
1 There are many other applications of the correlated count regression models in various ... Estimation of Hurdle Models for Overdispersed Count Data. The.
Modeling the Count Data of Public Health Service Visits with ...
The best model of negative binomial regression is selected by considering the values of AIC, BIC, Log-likelihood, and overdispersion tests that occur between ...
accounting for serial correlation in count models of traffic safety
In the presence of serial correlation, the efficiency of parameter estimates comes into question. Similar to the classical linear regression model, one can ...
Overdispersion tests dependence on used covariates in Poisson ...
Of course, the results of any overdispersion test can depend on the set of regressors employed. This is most easily seen in the cause with ...
Reparametrization of COM-Poisson Regression Models with ... - arXiv
This model provides a suitable strategy for the analysis of count data and an efficient Newton ... It can deal with both under and overdispersed ...
Testing for Overdispersion in Poisson and Binomial Regression ...
situation. KEY WORDS: Count data; Exponential family; Extra-binomial variation; Extra-Poisson variation; Mixture model; Proportion.
Zero-inflated negative binomial mixed regression modeling of over ...
Zero-inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros ... correlated due to the sampling design or the data ...
Modelling count data with overdispersion and spatial effects
Lambert (1992) introduced the zero inflated Poisson regression model, a Bayesian analysis of zero inflated models in general is given in ...
A New Regression Model for the Analysis of Overdispersed and ...
Count datasets are traditionally analyzed using the ordinary Poisson distribution. However, said model has its applicability limited, ...
Structured additive regression for overdispersed and zero-inflated ...
... count data regression models. Inference is fully Bayesian and is carried out by computationally efficient MCMC techniques. Simulation ...