Dynamic mixture-of-experts models for longitudinal and discrete-time survival data
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Cited by:
- Matias Quiroz & Robert Kohn & Mattias Villani & Minh-Ngoc Tran, 2019.
"Speeding Up MCMC by Efficient Data Subsampling,"
Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 831-843, April.
- Quiroz, Matias & Villani, Mattias & Kohn, Robert, 2015. "Speeding Up Mcmc By Efficient Data Subsampling," Working Paper Series 297, Sveriges Riksbank (Central Bank of Sweden).
- Kohn, Robert & Quiroz, Matias & Tran, Minh-Ngoc & Villani, Mattias, 2016. "Speeding up MCMC by Efficient Data Subsampling," Working Papers 2123/16205, University of Sydney Business School, Discipline of Business Analytics.
- Gunawan, David & Dang, Khue-Dung & Quiroz, Matias & Kohn, Robert & Tran, Minh-Ngoc, 2019. "Subsampling Sequential Monte Carlo for Static Bayesian Models," Working Paper Series 371, Sveriges Riksbank (Central Bank of Sweden).
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More about this item
Keywords
Bayesian inference; Markov Chain Monte Carlo; Bayesian variable selection; Survival Analysis; Mixture-of-experts;All these keywords.
JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
- D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
- G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2013-06-30 (Econometrics)
- NEP-FOR-2013-06-30 (Forecasting)
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