Statistical analysis with missing data
Statistical analysis with missing data
Parallel algorithms for computing all possible subset regression models using the QR decomposition
Parallel Computing - Special issue: Parallel computing in numerical optimization
Inference in Hidden Markov Models
Inference in Hidden Markov Models
Block sampler and posterior mode estimation for asymmetric stochastic volatility models
Computational Statistics & Data Analysis
Computational methods for complex stochastic systems: a review of some alternatives to MCMC
Statistics and Computing
On variance stabilisation in Population Monte Carlo by double Rao-Blackwellisation
Computational Statistics & Data Analysis
Interacting multiple try algorithms with different proposal distributions
Statistics and Computing
Bayesian inference for the multivariate skew-normal model: A population Monte Carlo approach
Computational Statistics & Data Analysis
Simulation-based Bayesian inference for epidemic models
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
Missing variable models are typical benchmarks for new computational techniques in that the ill-posed nature of missing variable models offer a challenging testing ground for these techniques. This was the case for the EM algorithm and the Gibbs sampler, and this is also true for importance sampling schemes. A population Monte Carlo scheme taking advantage of the latent structure of the problem is proposed. The potential of this approach and its specifics in missing data problems are illustrated in settings of increasing difficulty, in comparison with existing approaches. The improvement brought by a general Rao-Blackwellisation technique is also discussed.