The package: nonparametric regression using local rotation matrices in

The package implements nonparametric (smooth) regression for spherical data in , and is freely available from the Comprehensive Archive Network (CRAN), licensed under the MIT License. It can be use..

A tree-based approach to forming strata in multipurpose business surveys

Available in the .pdf fil

Spatial regressive and autoregressive models with SARMA error terms

The aim of this paper is to define and to analyse classes of models satisfactory for the consideration of the spatial effects of dependence and heterogeneity. In particular, we introduce the ADL-N (Autoregressive Distributed Lags - Noise) class in order to model sets of spatial data. This class consists of a generalization of the well known class of the ADL (Hendry et al., 1984) models, introduced in the analysis of time series. This generalization is obtained by means of the hypothesis that the error component is generated by a spatial ARMA process (Cressie, 1993). It is well known that the multilateral dependence, which characterizes spatial processes, results in correlation between the endogenous variable and the disturbance term in the model, causing the inconsistency of ordinary least squares estimators (Anselin, 1988). The model we propose is based upon the use of the conditional likelihood function to obtain consistent and asymptotically efficient estimators for the model's parameters. In the paper we report the gradient and the Hessian matrix of the log-likelihood function for the model proposed. To perform hypothesis testing, we have coded two procedures to test ML (Maximum Likelihood) estimation results, termed respectively OLT (Omitted Lags Test) and DLT (Deleted Lags Test). The latter tests the hypothesis that a component introduced in the model is inconsistent with the data; the former tests that a component not still introduced is - on the contrary - consistent with the data. All these procedures need the use of the Fisher Information matrix, which is reported for the ADL-N class of models

Strategie campionarie basate su modelli di superpopolazione con applicazioni al disegno di reti di monitoraggio ambientale

Dottorato di ricerca in statistica. 9. ciclo. Tutore G. ArbiaConsiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7, Rome; Biblioteca Nazionale Centrale - P.za Cavalleggeri, 1, Florence / CNR - Consiglio Nazionale delle RichercheSIGLEITItal

Spatial correlation estimates based on satellite observations corrected with the prior knowledge on sensor devices' technical characteristics

In many empirical studies spatial correlations are used to identify the distance above which dependency is negligible, to assist the choice in locating a systematic grid of sample points in ground surveys. However, estimates are undermined by the fact that our inference is based on satellite data that are only an approximation of the ground truth, due to the presence of a series of disturbing factors like (e.g.) light scattering, presence of obstacles (like clouds), and instrument precision limitations. In this paper we introduce a procedure to correct spatial correlation estimates using prior knowledge on the satellite sensor's technical characteristics and obtain more reliable estimates. We derive an approximation of the "ground-truth" pattern of correlation as a function of the satellite-based spatial correlation and of the sensor's (user-specified) technical characteristics. We show the effects of these corrections referring to a series of illustrative examples based on theoretical calculations regarding the negative exponential correlogram. The correction efficiency relative to the classical Method-of-Moments estimator is also assessed by means of a Monte Carlo application to simulated SAR maps

A regression tree algorithm for the identification of convergence clubs

The concept of convergence clubs is analyzed and compared with classical methods for the study of economic [beta]-convergence, which often consider the entire data set as one sample. A technique for the identification of convergence clubs is proposed. The algorithm is based on a modified version of the usual regression trees procedure. The objective function of the method is represented by the difference among the parameters of the model under investigation. Different strategies are adopted in the definition of the model used in the objective function of the algorithm. The first is the classical non-spatial [beta]-convergence model. The others are modified [beta]-convergence models which take into account the dependence showed by spatially distributed data. The proposed procedure identifies situation of local stationarity in the economic growth of the different regions: a group of regions is divided into two sub-groups if the parameter estimates are significantly different among them. The algorithm is applied to 191 European regions for the period 1980-2002. Given the adaptability of the algorithm, its implementation provides a flexible tool for the use of any regression model in the analysis of non-stationary spatial data.