Abstract

The analysis of regression data is often improved by using a transformation of the response rather than the original response itself. However, finding a suitable transformation can be strongly affected by the influence of a few individual observations. Outliers can have an enormous impact on the fitting of statistical models and can be hard to detect due to masking and swamping. These difficulties are enhanced in the case of models for dependent observations, since any anomalies are with respect to the specific autocorrelation structure of the model. In this paper we develop a forward search approach which is able to robustly estimate the Box-Cox transformation parameter under a first-order spatial autoregression model.  

Data used in the paper

First example (ascii format)

Second example (ascii format)

The ascii files have the following structure

x1 (first column of the file ) = horizontal coordinate

x2 (second column of the file) = vertical coordinate

x3 = (third column of the file) = y

Last modified 10/04/2017 17.25.14