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Robust Transformations and Outlier Detection with Autocorrelated Data

  • Andrea Cerioli
  • Marco Riani
Conference paper
  • 1.7k Downloads
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

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.

Keywords

Spatial Autocorrelation Score Statistic Forward Search Transformation Analysis Spatial Outlier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. ATKINSON, A.C. and RIANI, M. (2000): Robust Diagnostic Regression Analysis. Springer, New York.Google Scholar
  2. ATKINSON, A.C., RIANI, M. and CERIOLI, A. (2004): Exploring Multivariate Data with the Forward Search. Springer, New York.Google Scholar
  3. BOX, G.E.P. and COX, D.R. (1964): An Analysis of Transformations (with discussion). Journal of the Royal Statistical Society B, 26, 211–246.MathSciNetGoogle Scholar
  4. CERIOLI, A. and RIANI, M. (2002): Robust Methods for the Analysis of Spatially Autocorrelated Data. Statistical Methods and Applications-Journal of the Italian Statistical Society, 11, 335–358.Google Scholar
  5. CRESSIE, N.A.C. (1993): Statistics for Spatial Data. Wiley, New York.Google Scholar
  6. GRIFFITH, D.A. and LAYNE, L.J. (1999): A Casebook for Spatial Statistical Data Analysis. Oxford University Press, New York.Google Scholar
  7. PACE, R.K., BARRY, R., SLAWSON, V.C. Jr. and SIRMANS, C.F. (2004): Simultaneous Spatial and Functional Form Transformations. In: L. Anselin, R.J.G.M. Florax and S.J. Rey (Eds.): Advances in Spatial Econometrics. Springer, New York.Google Scholar
  8. RIPLEY, B.D. (1988): Statistical Inference for Spatial Processes. Cambridge University Press, Cambridge.Google Scholar
  9. ROUSSEEUW, P.J. and van ZOMEREN, B.C. (1990): Unmasking Multivariate Outliers and Leverage Points. Journal of the American Statistical Association, 85, 633–639.Google Scholar

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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