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This paper presents the R-package <B>MitISEM</B> (mixture of <I>t</I> by importance sampling weighted expectation maximization) which provides an automatic and flexible two-stage method to approximate a non-elliptical target density kernel -- typically a posterior density kernel -- using an adaptive mixture...</i></b>
Persistent link: https://www.econbiz.de/10011288392
This paper presents the R package MitISEM, which provides an automatic and flexible method to approximate a non-elliptical target density using adaptive mixtures of Student-t densities, where only a kernel of the target density is required. The approximation can be used as a candidate density in...
Persistent link: https://www.econbiz.de/10010326521
This paper presents the parallel computing implementation of the MitISEM algorithm, labeled Parallel MitISEM. The basic MitISEM algorithm, introduced by Hoogerheide, Opschoor and Van Dijk (2012), provides an automatic and flexible method to approximate a non-elliptical target density using...
Persistent link: https://www.econbiz.de/10011451514
This paper presents the R package MitISEM (mixture of t by importance sampling weighted expectation maximization) which provides an automatic and flexible two-stage method to approximate a non-elliptical target density kernel - typically a posterior density kernel - using an adaptive mixture of...
Persistent link: https://www.econbiz.de/10012143909
methods. The effects of several model characteristics(unit roots, GARCH, stochastic volatility, heavy tailed …
Persistent link: https://www.econbiz.de/10010324426
applied within a Bayesian analysisof a GARCH-mixture model which is used for the evaluation of theValue-at-Risk of the return …
Persistent link: https://www.econbiz.de/10010324702
methods. The effects ofseveral modelcharacteristics (unit roots, GARCH, stochastic volatility, heavy taileddisturbance …
Persistent link: https://www.econbiz.de/10010324963
Bayesian inference in a time series model provides exact, out-of-sample predictive distributions that fully and coherently incorporate parameter uncertainty. This study compares and evaluates Bayesian predictive distributions from alternative models, using as an illustration five alternative...
Persistent link: https://www.econbiz.de/10011605015
A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or linear pools, evaluated using the conventional log predictive scoring rule. The log score is a concave...
Persistent link: https://www.econbiz.de/10011605063
A class of adaptive sampling methods is introduced for efficient posterior and predictive simulation. The proposed methods are robust in the sense that they can handle target distributions that exhibit non-elliptical shapes such as multimodality and skewness. The basic method makes use of...
Persistent link: https://www.econbiz.de/10010325702