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Assessing influence in Gaussian long-memory models

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  • Palma, Wilfredo
  • Bondon, Pascal
  • Tapia, José

Abstract

A statistical methodology for detecting influential observations in long-memory models is proposed. The identification of these influential points is carried out by case-deletion techniques. In particular, a Kullback-Leibler divergence is considered to measure the effect of a subset of observations on predictors and smoothers. These techniques are illustrated with an analysis of the River Nile data where the proposed methods are compared to other well-known approaches such as the Cook and the Mahalanobis distances.

Suggested Citation

  • Palma, Wilfredo & Bondon, Pascal & Tapia, José, 2008. "Assessing influence in Gaussian long-memory models," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4487-4501, May.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:9:p:4487-4501
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    References listed on IDEAS

    as
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    7. Pascal Bondon, 2005. "Influence of Missing Values on the Prediction of a Stationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(4), pages 519-525, July.
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