Iterated importance sampling in missing data problems
From MaRDI portal
Publication:959418
DOI10.1016/j.csda.2005.07.018zbMath1445.62004OpenAlexW2057003005WikidataQ60461506 ScholiaQ60461506MaRDI QIDQ959418
Jean-Michel Marin, Gilles Celeux, Christian P. Robert Robert
Publication date: 11 December 2008
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2005.07.018
adaptive algorithmsBayesian inferencepopulation Monte Carlolatent variable modelsstochastic volatility modelRao-Blackwellisation
Computational methods for problems pertaining to statistics (62-08) Applications of statistics to actuarial sciences and financial mathematics (62P05) Missing data (62D10)
Related Items
Simulation-based Bayesian inference for epidemic models, Diagnostics of prior-data agreement in applied Bayesian analysis, A Survey of Sequential Monte Carlo Methods for Economics and Finance, An alternative competing risk model to the Weibull distribution for modelling aging in lifetime data analysis, Bayesian multiple quantile regression for linear models using a score likelihood, Iterated filtering, Online data processing: comparison of Bayesian regularized particle filters, Eliciting vague but proper maximal entropy priors in Bayesian experiments, Multilevel Monte Carlo in approximate Bayesian computation, Bayesian models for data missing not at random in health examination surveys, On variance stabilisation in population Monte Carlo by double Rao-Blackwellisation, Computational advances for and from Bayesian analysis, Convergence of adaptive mixtures of importance sampling schemes, Bayesian inference for the multivariate skew-normal model: a population Monte Carlo approach, Interacting multiple try algorithms with different proposal distributions, Block sampler and posterior mode estimation for asymmetric stochastic volatility models, Ensemble Transport Adaptive Importance Sampling, Sequential Monte Carlo Samplers
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Adaptive proposal distribution for random walk Metropolis algorithm
- Statistical methods in finance
- Markov chain Monte Carlo methods for stochastic volatility models.
- Inference in hidden Markov models.
- Sequential Monte Carlo Samplers
- Sampling-Based Approaches to Calculating Marginal Densities
- The Calculation of Posterior Distributions by Data Augmentation
- Rao-Blackwellisation of sampling schemes
- Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
- Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
- Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes
- Partial non-Gaussian state space
- Likelihood analysis of non-Gaussian measurement time series
- MODELING STOCHASTIC VOLATILITY: A REVIEW AND COMPARATIVE STUDY
- Computational and Inferential Difficulties with Mixture Posterior Distributions
- Adaptive Rejection Sampling for Gibbs Sampling
- Graphical models
- An adaptive Metropolis algorithm
- Parallel algorithms for linear models. Numerical methods and estimation problems