Search Results - subject:"expectation maximization"

Heterogeneity in loss aversion : evidence from field elicitations

Sproul, Thomas; Michaud, Clayton P. - In: Agricultural finance review 77 (2017) 1, pp. 196-216

Persistent link: https://www.econbiz.de/10011700285

Discrete-time optimal asset allocation under Higher-Order Hidden Markov Model

Zhu, Dong-Mei; Lu, Jiejun; Ching, Wai Ki; Siu, Tak Kuen - In: Economic modelling 66 (2017), pp. 223-232

Persistent link: https://www.econbiz.de/10011813727

A Class of Adaptive EM-based Importance Sampling Algorithms for Efficient and Robust Posterior and Predictive Simulation

Hoogerheide, Lennart; Opschoor, Anne; van Dijk, Herman K. - 2011

multimodality and skewness. The basic method makes use of sequences of importance weighted Expectation Maximization steps in order … target and mixture is minimized. We label this approach Mixture of t by Importance Sampling and Expectation Maximization …

Persistent link: https://www.econbiz.de/10010325702

An alternative to the Baum-Welch recursions for hidden Markov models

Bartolucci, Francesco - Volkswirtschaftliche Fakultät, … - 2011

parameter estimation within the Expectation-Maximization algorithm. The approach is illustrated by an application to nancial …

Persistent link: https://www.econbiz.de/10011112821

An efficient threshold choice for operational risk capital computation

Guegan, Dominique; Hassani, Bertrand; Naud, Cédric - HAL - 2011

Operational risk quantification requires dealing with data sets which often present extreme values which have a tremendous impact on capital computations (VaR). In order to take into account these effects we use extreme value distributions, and propose a two pattern model to characterize loss...

Persistent link: https://www.econbiz.de/10011025542

A Class of Adaptive EM-based Importance Sampling Algorithms for Efficient and Robust Posterior and Predictive Simulation

Hoogerheide, Lennart; Opschoor, Anne; Dijk, Herman K. van - Tinbergen Institute - 2011

multimodality and skewness. The basic method makes use of sequences of importance weighted Expectation Maximization steps in order … between target and mixture is minimized. We label this approach Mixture of <I>t</I> by Importance Sampling and Expectation … Maximization (MitISEM). We also introduce three extensions of the basic MitISEM approach. First, we propose a method for applying …

Persistent link: https://www.econbiz.de/10008838540

A Class of Adaptive EM-based Importance Sampling Algorithms for Efficient and Robust Posterior and Predictive Simulation

Hoogerheide, Lennart; Opschoor, Anne; Dijk, Herman K. van - Tinbergen Instituut - 2011

Persistent link: https://www.econbiz.de/10011256336

A class of adaptive EM-based importance sampling algorithms for efficient and robust posterior and predictive simulation

Hoogerheide, Lennart; Opschoor, Anne; Dijk, Herman K. van - 2011

Persistent link: https://www.econbiz.de/10011382695

A Novel Hybridization of Expectation-Maximization and K-Means Algorithms for Better Clustering Performance

Kishor, Duggirala Raja; Venkateswarlu, N.B. - In: International Journal of Ambient Computing and … 7 (2016) 2, pp. 47-74

Expectation Maximization (EM) is a widely employed mixture model-based data clustering algorithm and produces …

Persistent link: https://www.econbiz.de/10012042641

Riding with the four horsemen and the multivariate normal tempered stable model

Bianchi, Michele Leonardo; Tassinari, Gian Luca; … - In: International journal of theoretical and applied finance 19 (2016) 4, pp. 1-28

Persistent link: https://www.econbiz.de/10011523819