Results 101 to 110 of about 1,308 (195)

Discrete Response Multilevel Models for Repeated Measures: An Application to Voting Intentions Data

discrete response, longitudinal data, multilevel model, repeated measures, time series, underdispersion, voting intentions,
Maria Ferrao Barbosa, Harvey Goldstein
core   +1 more source

SAYMA VERİ MODELLERİ İLE ÇOCUK SAYISI BELİRLEYİCİLERİ: TÜRKİYE'DEKİ SEÇİLMİŞ İLLER İÇİN SOSYOEKONOMİK ANALİZLER [PDF]

, 2003
This paper models determinants of number of children in houshold by using Poisson Quasi Maximum Likelihood Methods. When dispersion considered, underdispersion is generally faced in \"the number of child\" data.
SELİM, Sibel, ÜÇDOĞRUK, ŞENAY
core  

Discrete dispersion models and their Tweedie asymptotics

, 2016
The paper introduce a class of two-parameter discrete dispersion models, obtained by combining convolution with a factorial tilting operation, similar to exponential dispersion models which combine convolution and exponential tilting.
Jørgensen, Bent, Kokonendji, Célestin C.   +1 more
core   +1 more source

Stationary Underdispersed INAR(1) Models based on the Backward Approach

Revstat Statistical Journal
Most of the stationary first-order autoregressive integer-valued (INAR(1)) models in the literature have been developed using the idea of binomial thinning. Two approaches have been adopted to establish the distributional properties of a stationary INAR(
Emad-Eldin A. A. Aly, Nadjib Bouzar
doaj   +1 more source

Too good to be true: Underdispersion in geochronology

Abstract. Statistical hypothesis testing is widely used in geochronology to assess whether multiple analyses of a sample are consistent with a single age. Failure of such tests is evidence for excess scatter ('overdispersion'), suggesting geological complexity or faulty data.
openaire   +1 more source

Supplementary Table 3

, 2015
Results of HMD (Homogeneity in Multivariate Dispersions) tests comparing live and dead diatom assemblages in total (littoral+open waters), littoral and open waters datasets showing: ratio (premortem variation/total LD variation), the estimates of ...
Gabriela S. Hassan (3311157)
core   +1 more source

CONWAY-MAXWELL POISSON REGRESSION MODELING OF INFANT MORTALITY IN SOUTH SULAWESI

Media Statistika
Overdispersion is a common problem in count data that can lead to inaccurate parameter estimates in Poisson regression models. Quasi-Poisson and negative binomial regressions are often used to address overdispersion but have limitations, especially with ...
Oktaviana Oktaviana, Wahidah Sanusi, Aswi Aswi, Sukarna Sukarna, Serifat Adedamola Folorunso   +4 more
doaj   +1 more source

Modeling and simulation of count data

, 2014
Count data, or number of events per time interval, are discrete data arising from repeated time to event observations. Their mean count, or piecewise constant event rate, can be evaluated by discrete probability distributions from the Poisson model ...
Plan, Elodie L,, Plan, Elodie L
core   +1 more source

Enhancing the accuracy of modeling highly multicollinear CO2 emission data using a novel generalized Poisson Liu regression method

AIP Advances
Count data often exhibit dispersion patterns that the standard Poisson regression model struggles to handle, particularly in cases of overdispersion or underdispersion. The generalized Poisson regression model (GPRM) provides a more flexible alternative,
Ali T. Hammad, Alexis Habineza, Ahmed M. Gemeay, Fatimah A. Almulhim   +3 more
doaj   +1 more source

Bivariate Random Coefficient Integer-Valued Autoregressive Model Based on a ρ-Thinning Operator

Axioms
While overdispersion is a common phenomenon in univariate count time series data, its exploration within bivariate contexts remains limited. To fill this gap, we propose a bivariate integer-valued autoregressive model.
Chang Liu, Dehui Wang
doaj   +1 more source