Weather modelling using a multivariate latent Gaussian model

We propose a vector autoregressive moving average process as a model for daily weather data. For the rainfall variable a monotonic transformation is applied to achieve marginal normality, thus defining a latent variable, with zero rainfall data corresponding to censored values below a threshold. Methodology is presented for model identification, estimation and validation, illustrated using data from Mynefield, Scotland. The new model, a VARMA(2,1) process, fits the data and produces more realistic simulated series than existing methods dur to Richardson (1981) and Peiris and McNicol (1996)

Smooth-car mixed models for spatial count data

Penalized splines (P-splines) and individual random effects are used for the analysis of spatial count data. P-splines are represented as mixed models to give a unified approach to the model estimation procedure. First, a model where the spatial variation is modelled by a two-dimensional P-spline at the centroids of the areas or regions is considered. In addition, individual area-effects are incorporated as random effects to account for individual variation among regions. Finally, the model is extended by considering a conditional autoregressive (CAR) structure for the random effects, these are the so called “Smooth-CAR” models, with the aim of separating the large-scale geographical trend, and local spatial correlation. The methodology proposed is applied to the analysis of lip cancer incidence rates in Scotland

Goodness of fit in models for mortality data

Mortality data on an aggregate level are characterized by very large sample sizes. For this reason, uninformative outcomes are evident in common Goodness-of-Fit measures. In this paper we propose a new measure that allows comparison of different mortality models even for large sample sizes. Particularly, we develop a measure which uses a null model specifically designed for mortality data. Several simulation studies and actual applications will demonstrate the performances of this new measure with special emphasis on demographic models and Pspline approach

P-spline anova-type interaction models for spatio-temporal smoothing

In recent years, spatial and spatio-temporal modelling have become an important area of research in many fields (epidemiology, environmental studies, disease mapping, ...). However, most of the models developed are constrained by the large amounts of data available. We propose the use of Penalized splines (P-splines) in a mixed model framework for smoothing spatio-temporal data. Our approach allows the consideration of interaction terms which can be decomposed as a sum of smooth functions similarly as an ANOVA decomposition. The properties of the bases used for regression allow the use of algorithms that can handle large amount of data. We show that imposing the same constraints as in a factorial design it is possible to avoid identifiability problems. We illustrate the methodology for Europe ozone levels in the period 1999-2005

Seasonal modulation mixed models for time series forecasting

We propose an extension of a seasonal modulation smooth model with P-splines for times series data using a mixed model formulation. A smooth trend with seasonality decomposition can be estimated simultaneously. We extend the model to consider the forecasting of new future observations in the mixed model framework. Two different approaches are used for forecasting in the context of mixed models, and the equivalence of both methods is shown. The methodology is illustrated with monthly sulphur dioxide (SO2) levels in a selection of monitoring sites in Europe from January 1990 to December 2001.This research was funded by the Spanish Ministry of Science and Innovation (projects MTM2008-02901, and MTM2011-28285-C02-02). The research of Dae-Jin Lee was also funded by an NIH grant for the Superfund Metal Mixtures, Biomarkers and Neurodevelopment project 1PA2ES016454-01A2

An introduction to pspatreg: A new R package for semiparametric spatial autoregressive analysis

This article introduces a new R package (pspatreg) for the estimation of semiparametric spatial autoregressive models. pspatreg fits penalized spline semiparametric spatial autoregressive models via Restricted Maximum Likelihood or Maximum Likelihood. These models are very flexible since they make it possible to simultaneously control for spatial dependence, nonlinearities in the functional form, and spatio-temporal heterogeneity. The package also allows to estimate parametric spatial autoregressive models for both cross sectional and panel data (with fixed effects), thus avoiding the use of different libraries. The official demos, vignettes, and tutorials of the package are distributed either in CRAN or GitHub. This article illustrates the potential of the  package by using an application to cross-sectional data

On the estimation of functional random effects

Functional regression modelling has become one of the most vibrant areas of research in the last years. This discussion provides some alternative approaches to one of the key issues of functional data analysis: the basis representation of curves, and in particular, of functional random effects. First, we propose the estimation of functional principal components by penalizing the norm, and as an alternative, we provide an efficient and unified approach based on B-spline basis and quadratic penalties

Modelling long term trend and local spatial correlation: a mixed penalized spline and spatial econometrics approach

In this work we propose the combination of P-splines with traditional spatial econometric models in such a way that it allows for their representation as a mixed model. The advantages of combining these models include: (i) dealing with complex non-linear and non-separable trends, (ii) estimating short-range spatial correlation together with the large-scale spatial trend, (iii) decomposing the systematic spatial variation into those two components and (iv) estimating the smoothing parameters included in the penalized splines together with the other parameters of the model. The performance of the proposed spatial non-parametric models is checked by both simulation and a empirical study. More specifically, we simulate 3,600 datasets generated by those models (with both linear and non-linear-non-separable global spatial trends). As for the empirical case, we use the well-known Lucas county data on housing prices. Our results indicate that the proposed models have a better performance than the traditional spatial strategies, specially in the presence of nonlinear tren

Desarrollo de un Sistema de Gestión de Proyectos mediante framework GWT

El proyecto a desarrollar consiste en una aplicación Web cuya finalidad será la de administrar y controlar los distintos proyectos desarrollados por una organización. Además, pretende cubrir la necesidad de establecer un seguimiento sobre las tareas desarrolladas por los distintos técnicos, con lo cual se cumple una doble finalidad: La primera es el control de las propias tareas en las que se descomponen los distintos módulos de cada proyecto de la organización y la segunda consiste en un control exhaustivo de las tareas desarrolladas por los distintos técnicos de la organización y el tiempo empleado en cada una de ellas. Por tanto, la aplicación puede proporcionar un mecanismo muy útil a la hora de realizar estimaciones para proyectos futuros, en cuanto a coste, complejidad, duración, etc., así como la posibilidad de medir el rendimiento y la productividad de los técnicos, que puede permitir a la organización optimizar futuros proyectos.Martínez Durbán, MR. (2012). Desarrollo de un Sistema de Gestión de Proyectos mediante framework GWT. http://hdl.handle.net/10251/16416.Archivo delegad

Prediction of functional data with spatial dependence: a penalized approach

This paper is focus on spatial functional variables whose observations are a set of spatially correlated sample curves obtained as realizations of a spatio-temporal stochastic process. In this context, as alternative to other geostatistical techniques (kriging, kernel smoothing, among others), a new method to predict the curves of temporal evolution of the process at unsampled locations and also the surfaces of geographical evolution of the variable at unobserved time points is proposed. In order to test the good performance of the proposed method, two simulation studies and an application with real climatological data have been carried out. Finally, the results were compared with ordinary functional kriging.Project P11-FQM-8068 from Consejería de Innovación, Ciencia y Empresa, Junta de Andalucía, SpainProjects MTM2013-47929-P, MTM2011-28285-C02-C2 and MTM 2014-52184-P from Secretaría de Estado Investigación, Desarrollo e Innovación, Ministerio de Economía y Competitividad, Spai