In analysing the distribution of a variable in a space, each value is subject not only to the source of the phenomenon but also to its localisation. In this paper, we t the model of the distribution, taking explicitly into account the spatial autocorrelation among the observed data. To this end we rst suppose that the observations are generated by a strong-mixing random eld. Then, after estimating the density of the considered variable, we construct a test statistics in order to verify the goodness of t of the observed spatial data. The proposed class of tests is a generalization of the classical chi-square-test and of the Neyman smooth test. The asymptotic behaviour of the test is analysed and some indications about its implementation are provided.
MODEL TESTING FOR SPATIAL STRONG-MIXING DATA
RIBECCO, Nunziata;
2004-01-01
Abstract
In analysing the distribution of a variable in a space, each value is subject not only to the source of the phenomenon but also to its localisation. In this paper, we t the model of the distribution, taking explicitly into account the spatial autocorrelation among the observed data. To this end we rst suppose that the observations are generated by a strong-mixing random eld. Then, after estimating the density of the considered variable, we construct a test statistics in order to verify the goodness of t of the observed spatial data. The proposed class of tests is a generalization of the classical chi-square-test and of the Neyman smooth test. The asymptotic behaviour of the test is analysed and some indications about its implementation are provided.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
