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A Class of Adaptive Importance Sampling Weighted EM Algorithms for Efficient and Robust Posterior and Predictive Simulation
Hoogerheide, Lennart F. - 2012
A class of adaptive sampling methods is introduced for efficient posterior and predictive simulation. The proposed … between target and mixture is minimized. We label this approach <I>Mixture of t by Importance Sampling and Expectation … Importance Sampling (IS) or the Metropolis-Hastings (MH) method. We also introduce three extensions of the basic MitISEM approach …
Persistent link: https://www.econbiz.de/10013108670
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Conditional Density Forecasting : A Tempered Importance Sampling Approach
Montes-Galdón, Carlos; Paredes, Joan; Wolf, Elias - 2022
proposed algorithm, which is based on tempered importance sampling, adapts the model-based density forecasts to target …
Persistent link: https://www.econbiz.de/10014237994
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Determination of Bayesian reliability sampling plans based on exponential-inverted gamma distribution
Appaia, Loganathan; Krishnan, Padmanaban Muthu; … - In: International journal of quality & reliability management 31 (2014) 8, pp. 950-962
Persistent link: https://www.econbiz.de/10010413629
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Bayesian calibration of generalized pools of predictive distributions
Casarin, Roberto; Mantoan, Giulia; Ravazzolo, Francesco - In: Econometrics : open access journal 4 (2016) 1, pp. 1-24
Decision-makers often consult different experts to build reliable forecasts on variables of interest. Combining more opinions and calibrating them to maximize the forecast accuracy is consequently a crucial issue in several economic problems. This paper applies a Bayesian beta mixture model to...
Persistent link: https://www.econbiz.de/10011505901
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Recovering the most entropic copulas from preliminary knowledge of dependence
Chu, Ba; Satchell, Stephen - In: Econometrics : open access journal 4 (2016) 2, pp. 1-21
This paper provides a new approach to recover relative entropy measures of contemporaneous dependence from limited information by constructing the most entropic copula (MEC) and its canonical form, namely the most entropic canonical copula (MECC). The MECC can effectively be obtained by...
Persistent link: https://www.econbiz.de/10011505976
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Bayesian nonparametric calibration and combination of predictive distributions
Bassetti, Federico; Casarin, Roberto; Ravazzolo, Francesco - 2015
Persistent link: https://www.econbiz.de/10011631783
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Bayesian semiparametric modeling of realized covariance matrices
Jin, Xin; Maheu, John M. - In: Journal of econometrics 192 (2016) 1, pp. 19-39
Persistent link: https://www.econbiz.de/10011610652
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Partially Censored Posterior for robust and efficient risk evaluation
Borowska, Agnieszka; Hoogerheide, Lennart; Koopman, Siem Jan - 2019
simulate model parameters from the Partially Censored Posterior, and PCP-QERMit, an Importance Sampling method that is …
Persistent link: https://www.econbiz.de/10012214294
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Partially censored posterior for robust and efficient risk evaluation
Borowska, Agnieszka; Hoogerheide, Lennart; Koopman, Siem Jan - In: Journal of econometrics 217 (2020) 2, pp. 335-355
Persistent link: https://www.econbiz.de/10012482776
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Partially censored posterior for robust and efficient risk evaluation
Borowska, Agnieszka; Hoogerheide, Lennart; Koopman, Siem Jan - 2019
simulate model parameters from the Partially Censored Posterior, and PCP-QERMit, an Importance Sampling method that is …
Persistent link: https://www.econbiz.de/10012057160