Quasi-Monte Carlo sampling to improve the efficiency of Monte Carlo EM
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Publication:957148
DOI10.1016/j.csda.2004.03.019zbMath1429.62021OpenAlexW1987325317MaRDI QIDQ957148
Publication date: 26 November 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.2004.03.019
Computational methods for problems pertaining to statistics (62-08) Point estimation (62F10) Monte Carlo methods (65C05) Geostatistics (86A32)
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Cites Work
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- On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals
- On the convergence properties of the EM algorithm
- Van der Corput sequences, Kakutani transforms and one-dimensional numerical integration
- Scrambling Sobol' and Niederreiter-Xing points
- Convergence of the Monte Carlo expectation maximization for curved exponential families.
- Randomized Halton sequences
- Methods for approximating integrals in statistics with special emphasis on Bayesian integration problems
- Quasi-Monte Carlo integration
- Maximum likelihood estimation via the ECM algorithm: A general framework
- A Unified View of the IPA, SF, and LR Gradient Estimation Techniques
- Discrépance de suites associées à un système de numération (en dimension s)
- Model-Based Geostatistics
- Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
- Laplace Importance Sampling for Generalized Linear Mixed Models
- Maximum Likelihood Algorithms for Generalized Linear Mixed Models
- Conditions for convergence of Monte Carlo EM sequences with an application to product diffusion modeling
- Safe and Effective Importance Sampling
- Hierarchical Models: A Current Computational Perspective
- An automated (Markov chain) Monte Carlo EM algorithm
- Approximate Inference in Generalized Linear Mixed Models
- Monte Carlo EM Estimation for Time Series Models Involving Counts
- Modeling uncertainty. An examination of stochastic theory, methods, and applications
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