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    Mixtures of skewed Kalman filters

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    Type
    Article
    Authors
    Kim, Hyoungmoon
    Ryu, Duchwan
    Mallick, Bani K.
    Genton, Marc G. cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Spatio-Temporal Statistics and Data Analysis Group
    Statistics Program
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2014-01
    Permanent link to this record
    http://hdl.handle.net/10754/563289
    
    Metadata
    Show full item record
    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.
    Citation
    Kim, H.-M., Ryu, D., Mallick, B. K., & Genton, M. G. (2014). Mixtures of skewed Kalman filters. Journal of Multivariate Analysis, 123, 228–251. doi:10.1016/j.jmva.2013.09.002
    Sponsors
    This publication is based in part on work supported by Award No. KUS-C1-016-04 made by King Abdullah University of Science and Technology (KAUST), and by NSF grant DMS-1007504. The first author's research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2013R1A1A2005995).
    Publisher
    Elsevier BV
    Journal
    Journal of Multivariate Analysis
    DOI
    10.1016/j.jmva.2013.09.002
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jmva.2013.09.002
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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