Mixtures of skewed Kalman filters

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

doi:10.1016/j.jmva.2013.09.002

Mixtures of skewed Kalman filters

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

Research output: Contribution to journal › Article › peer-review

13 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 language	English (US)
Pages (from-to)	228-251
Number of pages	24
Journal	Journal of Multivariate Analysis
Volume	123
DOIs	https://doi.org/10.1016/j.jmva.2013.09.002
State	Published - Jan 2014

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: 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).

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty
Numerical Analysis

Access to Document

10.1016/j.jmva.2013.09.002

Cite this

@article{e63d55c1b3dd48e09500bc45ab660d2c,

title = "Mixtures of skewed Kalman filters",

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. {\textcopyright} 2013 Elsevier Inc.",

author = "Hyoungmoon Kim and Duchwan Ryu and Mallick, {Bani K.} and Genton, {Marc G.}",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): KUS-C1-016-04 Acknowledgements: 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).",

year = "2014",

month = jan,

doi = "10.1016/j.jmva.2013.09.002",

language = "English (US)",

volume = "123",

pages = "228--251",

journal = "Journal of Multivariate Analysis",

issn = "0047-259X",

publisher = "Academic Press Inc.",

}

TY - JOUR

T1 - Mixtures of skewed Kalman filters

AU - Kim, Hyoungmoon

AU - Ryu, Duchwan

AU - Mallick, Bani K.

AU - Genton, Marc G.

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): KUS-C1-016-04 Acknowledgements: 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).

PY - 2014/1

Y1 - 2014/1

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/563289

UR - https://linkinghub.elsevier.com/retrieve/pii/S0047259X13001942

UR - http://www.scopus.com/inward/record.url?scp=84886804715&partnerID=8YFLogxK

U2 - 10.1016/j.jmva.2013.09.002

DO - 10.1016/j.jmva.2013.09.002

M3 - Article

SN - 0047-259X

VL - 123

SP - 228

EP - 251

JO - Journal of Multivariate Analysis

JF - Journal of Multivariate Analysis

ER -

Mixtures of skewed Kalman filters

Abstract

Bibliographical note

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this