Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
SHIFT AND SCALE COUPLING METHODS FOR PERFECT SIMULATION
Probability in the Engineering and Informational Sciences
Mixtures of skewed Kalman filters
Journal of Multivariate Analysis
A dynamic linear model with extended skew-normal for the initial distribution of the state parameter
Computational Statistics & Data Analysis
Kalman filter variants in the closed skew normal setting
Computational Statistics & Data Analysis
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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.