Andrea Cerioli | Marco Riani |
Dipartimento di Economia | Dipartimento di Economia |
Università degli Studi di Parma | Università degli Studi di Parma |
Italy | Italy |
andrea.cerioli@unipr.it | mriani@unipr.it |
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
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