An empirical evaluation of spatial regression models

作     者：Gao, Xiaolu Asami, Yasushi Chung, Chang-Jo F. 

作者机构：Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China Univ Tokyo Ctr Spatial Informat Sci Kashiwa Chiba 2778568 Japan Geol Survey Canada Ottawa ON K1A 0E8 Canada 

出 版 物：《COMPUTERS & GEOSCIENCES》 (计算机与地学)

年 卷 期：2006年第32卷第8期

页      面：1040-1051页

核心收录：

学科分类：08[工学] 0708[理学-地球物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Ministry of Education  Culture  Sports  Science and Technology  MEXT 

主　　题：spatial regression model model evaluation cross-validation prediction empirical criteria 

摘      要：Conventional statistical methods are often ineffective to evaluate spatial regression models. One reason is that spatial regression models usually have more parameters or smaller sample sizes than a simple model, so their degree of freedom is reduced. Thus, it is often unlikely to evaluate them based on traditional tests. Another reason, which is theoretically associated with statistical methods, is that statistical criteria are crucially dependent on such assumptions as normality, independence, and homogeneity. This may create problems because the assumptions are open for testing. In view of these problems, this paper proposes an alternative empirical evaluation method. To illustrate the idea, a few hedonic regression models for a house and land price data set are evaluated, including a simple, ordinary linear regression model and three spatial models. Their performance as to how well the price of the house and land can be predicted is examined. With a cross-validation technique, the prices at each sample point are predicted with a model estimated with the samples excluding the one being concerned. Then, empirical criteria are established whereby the predicted prices are compared with the real, observed prices. The proposed method provides an objective guidance for the selection of a suitable model specification for a data set. Moreover, the method is seen as an alternative way to test the significance of the spatial relationships being concerned in spatial regression models. (c) 2006 Published by Elsevier Ltd.