885 resultados para negative binomial
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Pós-graduação em Agronomia (Produção Vegetal) - FCAV
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Pós-graduação em Agronomia (Produção Vegetal) - FCAV
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Pós-graduação em Agronomia (Entomologia Agrícola) - FCAV
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Pós-graduação em Agronomia (Produção Vegetal) - FCAV
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Pós-graduação em Agronomia (Entomologia Agrícola) - FCAV
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Pós-graduação em Doenças Tropicais - FMB
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In this article we introduce a three-parameter extension of the bivariate exponential-geometric (BEG) law (Kozubowski and Panorska, 2005) [4]. We refer to this new distribution as the bivariate gamma-geometric (BGG) law. A bivariate random vector (X, N) follows the BGG law if N has geometric distribution and X may be represented (in law) as a sum of N independent and identically distributed gamma variables, where these variables are independent of N. Statistical properties such as moment generation and characteristic functions, moments and a variance-covariance matrix are provided. The marginal and conditional laws are also studied. We show that BBG distribution is infinitely divisible, just as the BEG model is. Further, we provide alternative representations for the BGG distribution and show that it enjoys a geometric stability property. Maximum likelihood estimation and inference are discussed and a reparametrization is proposed in order to obtain orthogonality of the parameters. We present an application to a real data set where our model provides a better fit than the BEG model. Our bivariate distribution induces a bivariate Levy process with correlated gamma and negative binomial processes, which extends the bivariate Levy motion proposed by Kozubowski et al. (2008) [6]. The marginals of our Levy motion are a mixture of gamma and negative binomial processes and we named it BMixGNB motion. Basic properties such as stochastic self-similarity and the covariance matrix of the process are presented. The bivariate distribution at fixed time of our BMixGNB process is also studied and some results are derived, including a discussion about maximum likelihood estimation and inference. (C) 2012 Elsevier Inc. All rights reserved.
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The scope of this paper was to analyze the association between homicides and public security indicators in Sao Paulo between 1996 and 2008, after monitoring the unemployment rate and the proportion of youths in the population. A time-series ecological study for 1996 and 2008 was conducted with Sao Paulo as the unit of analysis. Dependent variable: number of deaths by homicide per year. Main independent variables: arrest-incarceration rate, access to firearms, police activity. Data analysis was conducted using Stata. IC 10.0 software. Simple and multivariate negative binomial regression models were created. Deaths by homicide and arrest-incarceration, as well as police activity were significantly associated in simple regression analysis. Access to firearms was not significantly associated to the reduction in the number of deaths by homicide (p>0,05). After adjustment, the associations with both the public security indicators were not significant. In Sao Paulo the role of public security indicators are less important as explanatory factors for a reduction in homicide rates, after adjustment for unemployment rate and a reduction in the proportion of youths. The results reinforce the importance of socioeconomic and demographic factors for a change in the public security scenario in Sao Paulo.
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In this work, we correlate the daily number of human leptospirosis cases with several climatic factors. We used a negative binomial model that considers hospital daily admissions due to leptospirosis as the dependent variable, and the climatic variables of daily precipitation pattern, and maximum and minimum temperature as independent variables. We calculated the monthly leptospirosis admission probabilities from the precipitation and maximum temperature variables. The month of February showed the highest probability, although values were also high during the spring months. The month of February also showed the highest number of hospital admissions. Another interesting result is that, for every 20 mm precipitation, there was an average increase of 31.5% in hospital admissions. Additionally, the relative risk of leptospirosis varied from 1.1 to 2.0 when the precipitation varied from 20 to 140 mm.