Search results for "Count data"
showing 10 items of 11 documents
Overdispersion tests in count-data analysis.
2008
Count data are commonly assumed to have a Poisson distribution, especially when there is no diagnostic procedure for checking this assumption. However, count data rarely fit the restrictive assumptions of the Poisson distribution. The violation of much of such assumptions commonly results in overdispersion, which invalidates the Poisson distribution. Undetected overdispersion may entail important misleading inferences, so its detection is essential. In this study, different overdispersion diagnostic tests are evaluated through two simulation studies. In Exp. 1, the nominal error rate is compared under different sample sizes and Λ conditions. Analysis shows a remarkable performance of the χ…
Count data in psychological applied research.
2006
As some authors have noticed in fields other than psychology, level of measurement and distributional characteristics of count data are commonly not taken into account, so that they are analysed as normally distributed continuous variables, and therefore some general linear model is applied. In this work, we review a random sample of 457 articles published in the last four years in journals with the highest impact factor in the Journal Citation Reports (JCR Social Sciences Edition) of the Institute for Scientific Information. The goals are to know how often count variables appear in psychological applied research and which data analyses are used when dealing with count response variables. …
Accounting for dispersion and correlation in estimating Safety Performance Functions. An overview starting from a case study
2013
In statistical analysis of crash count data, as well as in estimating Safety Performance Functions (SPFs), the failure of Poisson equidispersion hypothesis and the temporal correlation in annual crash counts must be considered to improve the reliability of estimation of the parameters. After a short discussion on the statistical tools accounting for dispersion and correlation, the paper presents the methodological path followed in estimating a SPF for urban four-leg, signalized intersections. Since the case study exhibited signs of underdispersion, a Conway-Maxwell-Poisson Generalized Linear Model (GLM) was fitted to the data; then a quasi-Poisson model in the framework of Generalized Estim…
Comparison of the Andersen–Gill model with poisson and negative binomial regression on recurrent event data
2008
Many generalizations of the Cox proportional hazard method have been elaborated to analyse recurrent event data. The Andersen-Gill model was proposed to handle event data following Poisson processes. This method is compared with non-survival approaches, such as Poisson and negative binomial regression. The comparison is performed on data simulated according to various event-generating processes and differing in subject heterogeneity. When robust standard error estimates are applied, for Poisson processes the Andersen-Gill approach is comparable to a negative binomial regression, whereas the poisson regression has comparable coverage probabilities of confidence intervals, but increased type …
Spatial spillovers in France: a study on individual count data at the city level
2007
Our study aims to measure the effects of spatial R&D spillovers on firms' patent production at the city level. We use an original method to estimate the spatial dimension of spillovers using count data. The method, based on a generalized cross entropy approach, allows us to test spatial auto-correlation. The main result is that when there are local spillovers, their impact on knowledge production is different according to the geographical area and the sector.
Efficient Simulation of Multivariate Binomial and Poisson Distributions
1998
Power investigations, for example, in statistical procedures for the assessment of agreement among multiple raters often require the simultaneous simulation of several dependent binomial or Poisson distributions to appropriately model the stochastical dependencies between the raters' results. Regarding the rather large dimensions of the random vectors to be generated and the even larger number of interactions to be introduced into the simulation scenarios to determine all necessary information on their distributions' dependence stucture, one needs efficient and fast algorithms for the simulation of multivariate Poisson and binomial distributions. Therefore two equivalent models for the mult…
Assessment of Susceptibility Risk Factors for ADHD in Imaging Genetic Studies
2019
Objective: ADHD consists of a count of symptoms that often presents heterogeneity due to overdispersion and excess of zeros. Statistical inference is usually based on a dichotomous outcome that is underpowered. The main goal of this study was to determine a suited probability distribution to analyze ADHD symptoms in Imaging Genetic studies. Method: We used two independent population samples of children to evaluate the consistency of the standard probability distributions based on count data for describing ADHD symptoms. Results: We showed that the zero-inflated negative binomial (ZINB) distribution provided the best power for modeling ADHD symptoms. ZINB reveals a genetic variant, rs273342…
The dynamic interdependence in the demand of primary and emergency secondary care: A hidden Markov approach
2021
This paper develops an extension of the class of finite mixture models for longitudinal count data to the bivariate case by using a trivariate reduction technique and a hidden Markov chain approach. The model allows for disentangling unobservable time-varying heterogeneity from the dynamic effect of utilisation of primary and secondary care and measuring their potential substitution effect. Three points of supports adequately describe the distribution of the latent states suggesting the existence of three profiles of low, medium and high users who shows persistency in their behaviour, but not permanence as some switch to their neighbour's profile.
Handling Underdispersion in Calibrating Safety Performance Function at Urban, Four-Leg, Signalized Intersections
2011
Poisson basic assumption of equidispersion is often too much restrictive for crash count data; in fact this type of data has been found to often exhibit overdispersion. Underdispersion has been less commonly observed, and this is the reason why it has been less convenient to model directly than overdispersion. Overdispersion and underdispersion are not the only issues that can be a potential source of error in specifying statistical models and that can lead to biased crash-frequency predictions; these issues can derive from data properties (temporal and spatial correlation, time-varying explanatory variables, etc.) or from methodological approach (omitted variables, functional form selectio…