Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models
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References listed on IDEAS
- Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, December.
- Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, December.
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- Christian Brinch, 2012. "Efficient simulated maximum likelihood estimation through explicitly parameter dependent importance sampling," Computational Statistics, Springer, vol. 27(1), pages 13-28, March.
- Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
- Picchini, Umberto & Ditlevsen, Susanne, 2011. "Practical estimation of high dimensional stochastic differential mixed-effects models," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1426-1444, March.
- Pedersen, M.W. & Berg, C.W. & Thygesen, U.H. & Nielsen, A. & Madsen, H., 2011. "Estimation methods for nonlinear state-space models in ecology," Ecological Modelling, Elsevier, vol. 222(8), pages 1394-1400.
- Tore Selland Kleppe & Hans J. Skaug, 2008. "Building and Fitting Non‐Gaussian Latent Variable Models via the Moment‐Generating Function," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 664-676, December.
- Haiyan Zhao & Fred Huffer & Xu-Feng Niu, 2015. "Time-varying coefficient models with ARMA-GARCH structures for longitudinal data analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(2), pages 309-326, February.
- Skaug, Hans J. & Yu, Jun, 2014. "A flexible and automated likelihood based framework for inference in stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 642-654.
- Jo Eidsvik & Sara Martino & Håvard Rue, 2009. "Approximate Bayesian Inference in Spatial Generalized Linear Mixed Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 1-22, March.
- Hans J. Skaug & Jun Yu, 2009. "Automated Likelihood Based Inference for Stochastic Volatility Models," Working Papers 15-2009, Singapore Management University, School of Economics.
- Kleppe, Tore Selland & Skaug, Hans Julius, 2012. "Fitting general stochastic volatility models using Laplace accelerated sequential importance sampling," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3105-3119.
- Lee, Woojoo & Lim, Johan & Lee, Youngjo & del Castillo, Joan, 2011. "The hierarchical-likelihood approach to autoregressive stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 248-260, January.
- Kleppe, Tore Selland & Skaug, Hans J., 2008. "Simulated maximum likelihood for general stochastic volatility models: a change of variable approach," MPRA Paper 12022, University Library of Munich, Germany.
- Le Rouzic, Arnaud & Skaug, Hans J. & Hansen, Thomas F., 2010. "Estimating genetic architectures from artificial-selection responses: A random-effect framework," Theoretical Population Biology, Elsevier, vol. 77(2), pages 119-130.
- Bellio, Ruggero & Grassetti, Luca, 2011. "Semiparametric stochastic frontier models for clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 71-83, January.
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