A skewed Kalman filter

Philippe Naveau*, Marc Genton, Xilin Shen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

The popularity of state-space models comes from their flexibilities and the large variety of applications they have been applied to. For multivariate cases, the assumption of normality is very prevalent in the research on Kalman filters. To increase the applicability of the Kalman filter to a wider range of distributions, we propose a new way to introduce skewness to state-space models without losing the computational advantages of the Kalman filter operations. The skewness comes from the extension of the multivariate normal distribution to the closed skew-normal distribution. To illustrate the applicability of such an extension, we present two specific state-space models for which the Kalman filtering operations are carefully described.

Original languageEnglish (US)
Pages (from-to)382-400
Number of pages19
JournalJournal of Multivariate Analysis
Volume94
Issue number2
DOIs
StatePublished - Jun 1 2005

Keywords

  • Closed skew-normal distribution
  • State-space model

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

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