A weighted polynomial regression method for local fitting of spatial data |
| |
Authors: | Tonino Sclocco Marco Di Marzio |
| |
Institution: | (1) Dipartimento di Metodi Quantitativi e Teoria Economica, Universitá G. d Annunzio, Viale Pindaro, 42, 65127 Pescara, Italy |
| |
Abstract: | Typically, parametric approaches to spatial problems require restrictive assumptions. On the other hand, in a wide variety of practical situations nonparametric bivariate smoothing techniques has been shown to be successfully employable for estimating small or large scale regularity factors, or even the signal content of spatial data taken as a whole.We propose a weighted local polynomial regression smoother suitable for fitting of spatial data. To account for spatial variability, we both insert a spatial contiguity index in the standard formulation, and construct a spatial-adaptive bandwidth selection rule. Our bandwidth selector depends on the Geary s local indicator of spatial association. As illustrative example, we provide a brief Monte Carlo study case on equally spaced data, the performances of our smoother and the standard polynomial regression procedure are compared.This note, though it is the result of a close collaboration, was specifically elaborated as follows: paragraphs 1 and 2 by T. Sclocco and the remainder by M. Di Marzio. The authors are grateful to the referees for constructive comments and suggestions. |
| |
Keywords: | Adaptive bandwidth selection LISA Simulation Smoothing Spatial contiguity Spatial heterogenity |
本文献已被 SpringerLink 等数据库收录! |
|