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http://hdl.handle.net/1942/27306
Title: | Integrated nested Laplace approximation as a new estimation method for the combined model: a simulation study | Authors: | NEYENS, Thomas FAES, Christel MOLENBERGHS, Geert |
Issue Date: | 2019 | Publisher: | TAYLOR & FRANCIS INC | Source: | Communications in statistics. Simulation and computation, 48(3), p. 819-836 | Abstract: | The combined model accounts for different forms of extra-variability and has traditionally been applied in the likelihood framework, or in the Bayesian setting via Markov chain Monte Carlo. In this article, integrated nested Laplace approximation is investigated as an alternative estimation method for the combined model for count data, and compared with the former estimation techniques. Longitudinal, spatial, and multi-hierarchical data scenarios are investigated in three case studies as well as a simulation study. As a conclusion, integrated nested Laplace approximation provides fast and precise estimation, while avoiding convergence problems often seen when using Markov chain Monte Carlo. | Keywords: | Bayesian inference;Combined model;Count data;Integrated nested Laplace approximation;Spatial data analysis | Document URI: | http://hdl.handle.net/1942/27306 | ISSN: | 0361-0918 | e-ISSN: | 1532-4141 | DOI: | 10.1080/03610918.2017.1400053 | ISI #: | WOS:000465355800012 | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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