Nonparametric checks for count data models: an application to demand for health care in Spain

This paper presents model specification checking procedures for count data regression models which are consistent in the direction of nonparametric alternatives. The discussion is motivated in the context of a model of demand for health care in Spain. The parameters of the regression model are estimated by maximum likelihood based on Poisson and Negative Binomial specifications as well as by ordinary least squares and semiparametric generalized least squares. However, our interest is not only centered on the estimation ofthe regression parameters, but also the conditional probabilities of counts. Therefore, the specification of the conditional distribution function of counts is the main focus of attention. A useful preliminary diagnosis tool consists of comparing the conditional probabilities estimates by nonparametric regression and by maximum likelihood methods based on alternative models. We present formal specification procedures based on new developed testing methods for regression model checking. The test statistics are based on marked empirical processes which are not distribution free, but their critical values are well approximated by bootstrap. Such tests are valid for testing the functional form of the conditional mean and conditional probabilities resulting from alternative distributional specifications. In our health care demand model, the linear exponential regression model with a Negative Binomial seems to be appropiate for this data set

P-Adaptive Boundary Elements

This paper presents the implementation of an adaptive philosophy to plane potential problems, using the direct boundary element method. After some considerations about the state of the art and a discussion of the standard approach features, the possibility of separately treating the modelling of variables and their interpolation through hierarchical shape functions is analysed. Then the proposed indicators and estimators are given, followed by a description of a small computer program written for an IBM PC. Finally, some examples show the kind of results to be expected

The Interaction Between PDE and Graphs in Multiscale Modeling

In this article an upscaled model is presented, for complex networks with highly clustered regions exchanging some abstract quantities in both, microscale and macroscale level. Such an intricate system is approximated by a partitioned open map in $\mathbb{R}^{2}$ or $\mathbb{R}^{3}$. The behavior of the quantities is modeled as flowing in the map constructed and thus it is subject to be described by partial differential equations. We follow this approach using the Darcy Porous Media, saturated fluid flow model in mixed variational formulation.Comment: 14 pages, 4 figure