Fuzzy blocking regression models

Abstract

Regression analysis is a well known and a widely used technique in multivariate data analysis. The efficiency of it is extensively recognized. Recently, several proposed regression models have exploited the spatial classification structure of data. The purpose of this inclusion of the spatial classification structure is to set a heterogeneous data structure to homogeneous structure in order to adjust the heterogeneous data structure to a single regression model. One such method is a blocking regression model. However, the ordinal blocking regression model can not reflect the complex classification structure satisfactorily. Therefore, the fuzzy blocking regression models are offered to represent the classification structure by using fuzzy clustering methods. This chapter's focus is on the methods of the fuzzy clustering based blocking regression models. They are extensions of the conventional blocking regression model. © 2008 Springer-Verlag Berlin Heidelberg.