Quasi-Monte Carlo Methods for Binary Event Models with Complex Family Data
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| Názov: | Quasi-Monte Carlo Methods for Binary Event Models with Complex Family Data |
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| Autori: | Christoffersen, Benjamin, Mahjani, Behrang, Clements, Mark, Kjellström, Hedvig, 1973, Humphreys, Keith |
| Zdroj: | Journal of Computational And Graphical Statistics. 32(4):1393-1401 |
| Predmety: | Family-based studies, Generalized linear mixed model, Importance sampling |
| Popis: | The generalized linear mixed model for binary outcomes with the probit link function is used in many fields but has a computationally challenging likelihood when there are many random effects. We extend a previously used importance sampler, making it much faster in the context of estimating heritability and related effects from family data by adding a gradient and a Hessian approximation and making a faster implementation. Additionally, a graph-based method is suggested to simplify the likelihood when there are thousands of individuals in each family. Simulation studies show that the resulting method is orders of magnitude faster, has a negligible efficiency loss, and confidence intervals with nominal coverage. We also analyze data from a large study of obsessive-compulsive disorder based on Swedish multi-generational data. In this analysis, the proposed method yielded similar results to a previous analysis, but was much faster. Supplementary materials for this article are available online. |
| Popis súboru: | |
| Prístupová URL adresa: | https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-350088 https://doi.org/10.1080/10618600.2022.2151454 |
| Databáza: | SwePub |
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Nájsť tento článok vo Web of Science | Abstrakt: | The generalized linear mixed model for binary outcomes with the probit link function is used in many fields but has a computationally challenging likelihood when there are many random effects. We extend a previously used importance sampler, making it much faster in the context of estimating heritability and related effects from family data by adding a gradient and a Hessian approximation and making a faster implementation. Additionally, a graph-based method is suggested to simplify the likelihood when there are thousands of individuals in each family. Simulation studies show that the resulting method is orders of magnitude faster, has a negligible efficiency loss, and confidence intervals with nominal coverage. We also analyze data from a large study of obsessive-compulsive disorder based on Swedish multi-generational data. In this analysis, the proposed method yielded similar results to a previous analysis, but was much faster. Supplementary materials for this article are available online. |
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| ISSN: | 10618600 15372715 |
| DOI: | 10.1080/10618600.2022.2151454 |