Robust Transformations and Outlier Detection with Autocorrelated Data

  • Andrea Cerioli
  • Marco Riani
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


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.


Spatial Autocorrelation Score Statistic Forward Search Transformation Analysis Spatial Outlier 
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Copyright information

© Springer Berlin · Heidelberg 2006

Authors and Affiliations

  • Andrea Cerioli
    • 1
  • Marco Riani
    • 1
  1. 1.Department of Economics — Section of StatisticsUniversity of ParmaParmaItaly

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