Efficient simulated maximum likelihood estimation through explicitly parameter dependent importance sampling
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Publication:2512765
DOI10.1007/s00180-011-0230-zzbMath1304.65018OpenAlexW2039255102MaRDI QIDQ2512765
Publication date: 30 January 2015
Published in: Computational Statistics (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/11250/177750
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Cites Work
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- Efficient high-dimensional importance sampling
- Testing the assumptions behind importance sampling
- Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models
- Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
- Laplace Importance Sampling for Generalized Linear Mixed Models
- Maximum‐likelihood estimation for constrained‐ or missing‐data models
- Monte Carlo maximum likelihood estimation for non-Gaussian state space models
- Bias Correction in Generalized Linear Mixed Models With Multiple Components of Dispersion
- Maximum Likelihood Algorithms for Generalized Linear Mixed Models
- Another Look at the Salamander Mating Data: A Modified Laplace Approximation Approach
- Statistical algorithms for models in state space using SsfPack 2.2
- Bayesian Inference in Econometric Models Using Monte Carlo Integration
- Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives
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