Controlling for seasonal patterns and time varying confounders in time-series epidemiological models: a simulation study
(2014)
Journal Article
Perrakis, K., Gryparis, A., Schwartz, J., Tertre, A. L., Katsouyanni, K., Forastiere, F., Stafoggia, M., & Samoli, E. (2014). Controlling for seasonal patterns and time varying confounders in time-series epidemiological models: a simulation study. Statistics in Medicine, 33(28), 4904-4918. https://doi.org/10.1002/sim.6271
Outputs (3)
Bayesian inference for transportation origin-destination matrices: the Poisson-inverse Gaussian and other Poisson mixtures (2014)
Journal Article
Perrakis, K., Karlis, D., Cools, M., & Janssens, D. (2015). Bayesian inference for transportation origin-destination matrices: the Poisson-inverse Gaussian and other Poisson mixtures. Journal of the Royal Statistical Society: Series A, 178(1), 271-296. https://doi.org/10.1111/rssa.12057Transportation origin–destination analysis is investigated through the use of Poisson mixtures by introducing covariate‐based models which incorporate different transport modelling phases and also allow for direct probabilistic inference on link traf... Read More about Bayesian inference for transportation origin-destination matrices: the Poisson-inverse Gaussian and other Poisson mixtures.
On the use of marginal posteriors in marginal likelihood estimation via importance sampling (2014)
Journal Article
Perrakis, K., Ntzoufras, I., & Tsionas, E. G. (2014). On the use of marginal posteriors in marginal likelihood estimation via importance sampling. Computational Statistics & Data Analysis, 77, 54-69. https://doi.org/10.1016/j.csda.2014.03.004The efficiency of a marginal likelihood estimator where the product of the marginal posterior distributions is used as an importance sampling function is investigated. The approach is generally applicable to multi-block parameter vector settings, doe... Read More about On the use of marginal posteriors in marginal likelihood estimation via importance sampling.