Departamento de Estadística e Investigación Operativa
URI permanente para esta comunidadhttps://hdl.handle.net/10953/30
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Examinando Departamento de Estadística e Investigación Operativa por Materia "Count data"
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Ítem A modelling of the number of almazaras by municipality in Andalusia(2022) Cueva-López, Valentina; Rodríguez-Avi, José; Olmo-Jiménez, María José; Rodríguez-Reinoso, JuliaAn almazara (oil mill) is an essential piece in the production of olive oil since it is the place where the olive is milled and the olive oil is obtained. They are usually linked to producer cooperatives. They are structures that require specialized machinery and that on multiple occasions are underutilised, given the presence of several of them at very close distances. In addition, they characterise the mainly olive grove municipalities and their study provides a valuable information of economic interest. From a statistical point of view, the “number of oil mills per municipality” is a count data variable that exhibits overdispersion. In this study, we focus on the oil mills found in municipalities of Andalusia. First, we make a descriptive study of the variable. Second, we model this data according to the most suitable probabilistic model. Finally, several generalized linear regression models based on different geographic and socioeconomic variables are proposed and the best one (using the Akaike information criterion) is selected.Ítem A new approach to truncated regression for count data(Springer, 2018-12-10) Martínez-Rodríguez, Ana M.; Conde-Sánchez, Antonio; Olmo-Jiménez, María J.Standard Poisson and negative binomial truncated regression models for count data include the regressors in the mean of the non-truncated distribution. In this paper, a new approach is proposed so that the explanatory variables determine directly the truncated mean. The main advantage is that the regression coefficients in the new models have a straightforward interpretation as the effect of a change in a covariate on the mean of the response variable. A simulation study has been carried out in order to analyze the performance of the proposed truncated regression models versus the standard ones showing that coefficient estimates are now more accurate in the sense that the standard errors are always lower. Also, the simulation study indicates that the estimates obtained with the standard models are biased. An application to real data illustrates the utility of the introduced truncated models in a hurdle model. Although in the example there are slight differences in the results between the two approaches, the proposed one provides a clear interpretation of the coefficient estimates.Ítem DGLMExtPois: Advances in Dealing with Over and Under-dispersion in a Double GLM Framework(The R Foundation, 2022-12) Sáez-Castillo, Antonio J.; Conde-Sánchez, Antonio; Martínez, FranciscoIn recent years the use of regression models for under-dispersed count data, such as COM-Poisson or hyper-Poisson models, has increased. In this paper the DGLMExtPois package is presented. DGLMExtPois includes a new procedure to estimate the coefficients of a hyper-Poisson regression model within a GLM framework. The estimation process uses a gradient-based algorithm to solve a nonlinear constrained optimization problem. The package also provides an implementation of the COM-Poisson model, proposed by Huang (2017), to make it easy to compare both models. The functionality of the package is illustrated by fitting a model to a real dataset. Furthermore, an experimental comparison is made with other related packages, although none of these packages allow you to fit a hyper-Poisson model.