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Vehicle Crash Frequency Analysis Using Poisson Regression Negative Binomial Regression And Neural Network

This article is from BMC Oral Health , volume 13 . Abstract Background: Limited information on oral health status for young adults aged 18 year-olds is known, and no available data exists in Hong Kong. The aims of this study were to investigate the oral health status and its risk indicators among young adults in Hong Kong using negative binomial regression. Methods: A survey was conducted in a representative sample of Hong Kong young adults aged 18 years. Clinical examinations were taken to assess oral health status using DMFT index and Community Periodontal Index (CPI) according to WHO criteria. Negative binomial regressions for DMFT score and the number of sextants with healthy gums were performed to identify the risk indicators of oral health status. Results: A total of 324 young adults were examined. Prevalence of dental caries experience among the subjects was 59% and the overall mean DMFT score was 1.4. Most subjects (95%) had a score of 2 as their highest CPI score. Negative binomial regression analyses revealed that subjects who had a dental visit within 3 years had significantly higher DMFT scores (IRR = 1.68, p 

This article introduces a k-Inflated Negative Binomial mixture distribution/regression model as a more flexible alternative to zero-inflated Poisson distribution/regression model. An EM algorithm has been employed to estimate the model's parameters. Then, such new model along with a Pareto mixture model have been employed to design an optimal rate--making system. Namely, this article employs number/size of reported claims of Iranian third party insurance dataset. Then, it employs the k-Inflated Negative Binomial mixture distribution/regression model as well as other well developed counting models along with a Pareto mixture model to model frequency/severity of reported claims in Iranian third party insurance dataset. Such numerical illustration shows that: ({\bf 1}) the k-Inflated Negative Binomial mixture models provide more fair rate/pure premiums for policyholders under a rate--making system; and ({\bf 2}) in the situation that number of reported claims uniformly distributed in past experience of a policyholder (for instance $k_1=1$ and $k_2=1$ instead of $k_1=0$ and $k_2=2$). The rate/pure premium under the k-Inflated Negative Binomial mixture models are more appealing and acceptable.

In this paper, the third in a series illustrating the power of generalized linear models (GLMs) for the astronomical community, we elucidate the potential of the class of GLMs which handles count data. The size of a galaxy's globular cluster population $N_{\rm GC}$ is a prolonged puzzle in the astronomical literature. It falls in the category of count data analysis, yet it is usually modelled as if it were a continuous response variable. We have developed a Bayesian negative binomial regression model to study the connection between $N_{\rm GC}$ and the following galaxy properties: central black hole mass, dynamical bulge mass, bulge velocity dispersion, and absolute visual magnitude. The methodology introduced herein naturally accounts for heteroscedasticity, intrinsic scatter, errors in measurements in both axes (either discrete or continuous), and allows modelling the population of globular clusters on their natural scale as a non-negative integer variable. Prediction intervals of 99% around the trend for expected $N_{\rm GC}$comfortably envelope the data, notably including the Milky Way, which has hitherto been considered a problematic outlier. Finally, we demonstrate how random intercept models can incorporate information of each particular galaxy morphological type. Bayesian variable selection methodology allows for automatically identifying galaxy types with different productions of GCs, suggesting that on average S0 galaxies have a GC population 35% smaller than other types with similar brightness.

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tuvev molululaj pabez merok falikavapo

Count data, for example the number of observed cases of a disease in a city, often arise in the fields of healthcare analytics and epidemiology. In this paper, we consider performing regression on multivariate data in which our outcome is a count. Specifically, we derive log-likelihood functions for finite mixtures of regression models involving counts that come from a Poisson distribution, as well as a negative binomial distribution when the counts are significantly overdispersed. Within our proposed modeling framework, we carry out optimal component selection using the information criteria scores AIC, BIC, CAIC, and ICOMP. We demonstrate applications of our approach on simulated data, as well as on a real data set of HIV cases in Tennessee counties from the year 2010. Finally, using a genetic algorithm within our framework, we perform variable subset selection to determine the covariates that are most responsible for categorizing Tennessee counties. This leads to some interesting insights into the traits of counties that have high HIV counts.