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http://hdl.handle.net/1942/27306Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | NEYENS, Thomas | - |
| dc.contributor.author | FAES, Christel | - |
| dc.contributor.author | MOLENBERGHS, Geert | - |
| dc.date.accessioned | 2018-11-08T12:19:43Z | - |
| dc.date.available | 2018-11-08T12:19:43Z | - |
| dc.date.issued | 2019 | - |
| dc.identifier.citation | Communications in statistics. Simulation and computation, 48(3), p. 819-836 | - |
| dc.identifier.issn | 0361-0918 | - |
| dc.identifier.uri | http://hdl.handle.net/1942/27306 | - |
| dc.description.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. | - |
| dc.description.sponsorship | Financial support from the IAP research network #P7/06 of the Belgian Government (Belgian Science Policy) and the Research Foundation Flanders (12S7217N) is gratefully acknowledged. | - |
| dc.language.iso | en | - |
| dc.publisher | TAYLOR & FRANCIS INC | - |
| dc.subject.other | Bayesian inference | - |
| dc.subject.other | Combined model | - |
| dc.subject.other | Count data | - |
| dc.subject.other | Integrated nested Laplace approximation | - |
| dc.subject.other | Spatial data analysis | - |
| dc.title | Integrated nested Laplace approximation as a new estimation method for the combined model: a simulation study | - |
| dc.type | Journal Contribution | - |
| dc.identifier.epage | 836 | - |
| dc.identifier.issue | 3 | - |
| dc.identifier.spage | 819 | - |
| dc.identifier.volume | 48 | - |
| local.bibliographicCitation.jcat | A1 | - |
| local.publisher.place | 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA | - |
| dc.relation.references | Aregay,M., Z. Shkedy, and G.Molenberghs. 2013. A hierarchical Bayesian approach for the analysis of longitudinal count data with overdispersion: A simulation study. Computational Statistics & Data Analysis 57:233–45. Besag, J., and C.Kooperberg. 1995. On conditional and intrinsic autoregressions. Biometrika 82:733–46. Booth, J. G., G. Casella, H. Friedl, and J. P. Hobert. 2003. Negative binomial loglinear mixed models. Statistical Modelling 3:179–81. Brooks, S. P., and A. Gelman. 1997. GeneralMethods forMonitoring Convergence of Iterative Simulations. Journal of Computational and Graphical Statistics 7:434–55. Efendi,A., G.Molenberghs, E. N.Njagi, and P.Dendale. 2013. A jointmodel for longitudinal continuous and time-to-event outcomes with direct marginal interpretation. Biometrical Journal 55:572–88. Faes, C., J. T. Ormerod, and M. P. Wand. 2011. Variational Bayesian inference for parametric and nonparametric regression with missing data. 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Gender-specific differences and the impact of family integration on time trends in age-stratified Swiss suicide rates. Journal of the Royal Statistical Society 175:473–90. Ripley, B. D. 1987. Stochastic Simulation. New York: Wiley. Rue, H., S. Martino, and N. Chopin. 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society 71: 319–92. Schrödle, B., and L. Held. 2010. Spatio-temporal disease mapping using INLA. Environmetrics 22: 725–34. Taylor, B. M., and P. J. Diggle. 2014. INLA or MCMC? A tutorial and comparative evaluation for spatial prediction in log-Gaussian Cox processes. Journal of Statistical Computation and Simulation 84:2266–84. DOI: 10.1080/00949655.2013.788653. | - |
| local.type.refereed | Refereed | - |
| local.type.specified | Article | - |
| dc.source.type | Article | - |
| dc.identifier.doi | 10.1080/03610918.2017.1400053 | - |
| dc.identifier.isi | WOS:000465355800012 | - |
| dc.identifier.eissn | 1532-4141 | - |
| local.provider.type | Web of Science | - |
| local.uhasselt.international | no | - |
| item.contributor | NEYENS, Thomas | - |
| item.contributor | FAES, Christel | - |
| item.contributor | MOLENBERGHS, Geert | - |
| item.fulltext | With Fulltext | - |
| item.accessRights | Restricted Access | - |
| item.fullcitation | NEYENS, Thomas; FAES, Christel & MOLENBERGHS, Geert (2019) Integrated nested Laplace approximation as a new estimation method for the combined model: a simulation study. In: Communications in statistics. Simulation and computation, 48(3), p. 819-836. | - |
| crisitem.journal.issn | 0361-0918 | - |
| crisitem.journal.eissn | 1532-4141 | - |
| Appears in Collections: | Research publications | |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| LSSP_A_1400053_O.pdf Restricted Access | Early view | 862.69 kB | Adobe PDF | View/Open Request a copy |
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