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Biometrika 1999 86(4):965-972; doi:10.1093/biomet/86.4.965
© 1999 by Biometrika Trust
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Miscellanea. Statistical analysis of incomplete long-range dependent data

W PalmaA1 and G del PinoA2

Departamento de Estadistica, Pontificia Universidad de Católica de Chile, Casilla 306, Santiago 22, Chile A1 E-mail: wilfredo@mat.puc.cl A2 E-mail: gdelpino@mat.puc.cl

This paper addresses both theoretical and methodological issues related to the prediction of long-memory models with incomplete data. Estimates and forecasts are calculated by means of state space models and the influence of data gaps on the performance of short and long run predictions is investigated. These techniques are illustrated with a statistical analysis of the minimum water levels of the Nile river, a time series exhibiting strong dependency.

Key Words: ARFIMA model; incomplete data; linear predictor; long-memory; maximum likelihood; mean square prediction error; state space system


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