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Modeling underdispersed count data with generalized Poisson regression

Harris, Tammy; Yang, Zhao; Hardin, James W. - In: Stata Journal 12 (2012) 4, pp. 736-747

We present motivation and new Stata commands for modeling count data. While the focus of this article is on modeling data with underdispersion, the new command for fitting generalized Poisson regression models is also suitable as an alternative to negative binomial regression for overdispersed data.

Persistent link: https://www.econbiz.de/10010630741

A combined overdispersed and marginalized multilevel model

Iddi, Samuel; Molenberghs, Geert - In: Computational Statistics & Data Analysis 56 (2012) 6, pp. 1944-1951

Overdispersion and correlation are two features often encountered when modeling non-Gaussian dependent data, usually as … acknowledges the presence of dependency and overdispersion by way of two separate sets of random effects. A marginally specified … comfort of marginally meaningful parameters and the ease of allowing for overdispersion and correlation. Apart from model …

Persistent link: https://www.econbiz.de/10010574444

A hierarchical Bayesian approach for the analysis of longitudinal count data with overdispersion: A simulation study

Aregay, Mehreteab; Shkedy, Ziv; Molenberghs, Geert - In: Computational Statistics & Data Analysis 57 (2013) 1, pp. 233-245

In sets of count data, the sample variance is often considerably larger or smaller than the sample mean, known as a problem of over- or underdispersion. The focus is on hierarchical Bayesian modeling of such longitudinal count data. Two different models are considered. The first one assumes a...

Persistent link: https://www.econbiz.de/10010580853

Reduced-rank vector generalized linear models with two linear predictors

Yee, Thomas W. - In: Computational Statistics & Data Analysis 71 (2014) C, pp. 889-902

Vector generalized linear models (VGLMs) as implemented in the vgamR package permit multiple parameters to depend (via inverse link functions) on linear predictors. However it is often the case that one wishes different parameters to be related to each other in some way (i.e., to jointly satisfy...

Persistent link: https://www.econbiz.de/10010719656

Zero-modified discrete distributions for operational risk modelling

Mouatassim, Younes - In: Journal of Risk Finance 13 (2012), pp. 476-490

Purpose – The purpose of this paper is to introduce the zero-modified distributions in the calculation of operational value-at-risk. Design/methodology/approach – This kind of distributions is preferred when excess of zeroes is observed. In operational risk, this phenomenon may be due to the...

Persistent link: https://www.econbiz.de/10010720095

Useful models for time series of counts or simply wrong ones?

Jung, Robert; Tremayne, A. - In: AStA Advances in Statistical Analysis 95 (2011) 1, pp. 59-91

Persistent link: https://www.econbiz.de/10008925208

Simulation-Based Estimation of the Structural Errors-in-Variables Negative Binomial Regression Model with an Application

Guo, Jie Q.; Li, Tong - In: Annals of Economics and Finance 2 (2001) 1, pp. 101-122

in the presence of measurement errors, in general, maximum likelihood estimator of the overdispersion using the observed …

Persistent link: https://www.econbiz.de/10009145687

Nonparametric estimation of mean and dispersion functions in extended generalized linear models

Gijbels, I.; Prosdocimi, I.; Claeskens, G. - In: TEST: An Official Journal of the Spanish Society of … 19 (2010) 3, pp. 580-608

Persistent link: https://www.econbiz.de/10008775823

A General Family of Limited Information Goodness-of-Fit Statistics for Multinomial Data

Joe, Harry; Maydeu-Olivares, Alberto - In: Psychometrika 75 (2010) 3, pp. 393-419

Persistent link: https://www.econbiz.de/10008674007

Modeling Site Specific Heterogeneity in an On-Site Stratified Random Sample of Recreational Demand

Sardana, Kavita; Bergstrom, John C. - Agricultural and Applied Economics Association - AAEA - 2011

Using estimation of demand for the George Washington/Jefferson National Forest as a case study, it is shown that in a stratified/clustered on-site sample, latent heterogeneity needs to be accounted for twice: first to account for dispersion in the data caused by unobservability of the process...

Persistent link: https://www.econbiz.de/10009021105