Mixtures of skewed Kalman filters

Hyoungmoon Kim, Duchwan Ryu, Bani K. Mallick, Marc G. Genton

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Normal state-space models are prevalent, but to increase the applicability of the Kalman filter, we propose mixtures of skewed, and extended skewed, Kalman filters. To do so, the closed skew-normal distribution is extended to a scale mixture class of closed skew-normal distributions. Some basic properties are derived and a class of closed skew. t distributions is obtained. Our suggested family of distributions is skewed and has heavy tails too, so it is appropriate for robust analysis. Our proposed special sequential Monte Carlo methods use a random mixture of the closed skew-normal distributions to approximate a target distribution. Hence it is possible to handle skewed and heavy tailed data simultaneously. These methods are illustrated with numerical experiments. © 2013 Elsevier Inc.
Original languageEnglish (US)
Pages (from-to)228-251
Number of pages24
JournalJournal of Multivariate Analysis
Volume123
DOIs
StatePublished - Jan 2014

ASJC Scopus subject areas

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

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