A review of Bayesian variable selection methods: what, how and which

O’Hara, Robert B.; Sillanpää, Mikko J.

doi:10.1214/09-ba403

Cited by 620 publications

(496 citation statements)

References 15 publications

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“…One results from using Jeffreys' prior and the other from the use of indicator variables (see O'Hara and Sillanpää, 2009). Based on our experiments, Jeffreys' prior dominates the other source of sparseness.…”

Section: Model Extensions and Mcmc Estimationmentioning

confidence: 79%

Correcting for relatedness in Bayesian models for genomic data association analysis

Pikkuhookana

Sillanpää

2009

Heredity

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For small pedigrees, the issue of correcting for known or estimated relatedness structure in population-based Bayesian multilocus association analysis is considered. Two such relatedness corrections: [1] a random term arising from the infinite polygenic model and [2] a fixed covariate following the class D model of Bonney, are compared with the case of no correction using both simulated and real marker and geneexpression data from lymphoblastoid cell lines from four CEPH families. This comparison is performed with clinical quantitative trait locus (cQTL) models-multilocus association models where marker data and expression levels of gene transcripts as well as possible genotype Â expression interaction terms are jointly used to explain quantitative trait variation. We found out that regardless of having a correction term in the model, the cQTL-models fit a few extra smalleffect components (similar to finite polygenic models) which itself serves as a relatedness correction. For small data and small heritability one may use the covariate model, which clearly outperforms the infinite polygenic model in small data examples.

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Section: Model Extensions and Mcmc Estimationmentioning

confidence: 79%

Correcting for relatedness in Bayesian models for genomic data association analysis

Pikkuhookana

Sillanpää

2009

Heredity

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“…Although no single model-selection approach for hierarchical models has yet seen wide application [13], it is an area of ongoing development and advancement (e.g., [94]). Model selection might present challenges, but variable selection can be done through various methods [95].…”

Section: Box 3 Assumptions and Limitations Of Msomsmentioning

confidence: 99%

Detecting diversity: emerging methods to estimate species diversity

Iknayan

Tingley

Furnas

et al. 2014

Trends in Ecology & Evolution

291

393

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“…For a review see O'Hara and Sillanpää (2009). Although these methods can be considered superior in terms of providing a unique solution, we opted for a methodology that allowed us to keep under control the issues of multi-collinearity characteristic for time-series analysis and tendency survey data.…”

Section: Forecasting Modelsmentioning

confidence: 99%

Bayesian Averaging vs. Dynamic Factor Models for Forecasting Economic Aggregates with Tendency Survey Data

2015

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The article compares forecast quality from two atheoretical models. Neither method assumed a priori causality and forecasts were generated without additional assumptions about regressors. Tendency survey data was used within the Bayesian averaging of classical estimates (BACE) framework and dynamic factor models (DFM). Two methods for regressor selection were applied within the BACE framework: frequentist averaging (BA) and frequentist (BF) with a collinearity-corrected version of the latter (BFC). Since models yielded multiple forecasts for each period, an approach to combine them was implemented. Results were assessed using in-and out-of-sample prediction errors. Although results did not vary significantly, best performance was observed from Bayesian models adopting the frequentist approach. Forecast of the unemployment rate were generated with the highest precision, followed by rate of GDP growth and CPI. It can be concluded that although these methods are atheoretical, they provide reasonable forecast accuracy, no worse to that expected from structural models. A further advantage to this approach is that much of the forecast procedure can be automated and much influence from subjective decisions avoided. JEL C10 C38 C83 E32 E37

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A review of Bayesian variable selection methods: what, how and which

Cited by 620 publications

References 15 publications

Correcting for relatedness in Bayesian models for genomic data association analysis

Correcting for relatedness in Bayesian models for genomic data association analysis

Detecting diversity: emerging methods to estimate species diversity

Bayesian Averaging vs. Dynamic Factor Models for Forecasting Economic Aggregates with Tendency Survey Data

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