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 -