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Two-level stochastic search variable selection in GLMs with missing predictors

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Two-level stochastic search variable selection in GLMs with missing predictors. / Mitra, Robin; Dunson, David.
In: International Journal of Biostatistics, Vol. 6, No. 1, 01.01.2010.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Mitra, R & Dunson, D 2010, 'Two-level stochastic search variable selection in GLMs with missing predictors', International Journal of Biostatistics, vol. 6, no. 1. https://doi.org/10.2202/1557-4679.1173

APA

Mitra, R., & Dunson, D. (2010). Two-level stochastic search variable selection in GLMs with missing predictors. International Journal of Biostatistics, 6(1). https://doi.org/10.2202/1557-4679.1173

Vancouver

Mitra R, Dunson D. Two-level stochastic search variable selection in GLMs with missing predictors. International Journal of Biostatistics. 2010 Jan 1;6(1). doi: 10.2202/1557-4679.1173

Author

Mitra, Robin ; Dunson, David. / Two-level stochastic search variable selection in GLMs with missing predictors. In: International Journal of Biostatistics. 2010 ; Vol. 6, No. 1.

Bibtex

@article{63eeca250f094dd594fe2f54f9bbe74d,
title = "Two-level stochastic search variable selection in GLMs with missing predictors",
abstract = "Stochastic search variable selection (SSVS) algorithms provide an appealing and widely used approach for searching for good subsets of predictors while simultaneously estimating posterior model probabilities and model-averaged predictive distributions. This article proposes a two-level generalization of SSVS to account for missing predictors while accommodating uncertainty in the relationships between these predictors. Bayesian approaches for allowing predictors that are missing at random require a model on the joint distribution of the predictors. We show that predictive performance can be improved by allowing uncertainty in the specification of predictor relationships in this model. The methods are illustrated through simulation studies and analysis of an epidemiologic data set.",
keywords = "missing at random, model averaging, multiple imputation, stochastic search, subset, variable selection",
author = "Robin Mitra and David Dunson",
year = "2010",
month = jan,
day = "1",
doi = "10.2202/1557-4679.1173",
language = "English",
volume = "6",
journal = "International Journal of Biostatistics",
publisher = "Walter de Gruyter GmbH",
number = "1",

}

RIS

TY - JOUR

T1 - Two-level stochastic search variable selection in GLMs with missing predictors

AU - Mitra, Robin

AU - Dunson, David

PY - 2010/1/1

Y1 - 2010/1/1

N2 - Stochastic search variable selection (SSVS) algorithms provide an appealing and widely used approach for searching for good subsets of predictors while simultaneously estimating posterior model probabilities and model-averaged predictive distributions. This article proposes a two-level generalization of SSVS to account for missing predictors while accommodating uncertainty in the relationships between these predictors. Bayesian approaches for allowing predictors that are missing at random require a model on the joint distribution of the predictors. We show that predictive performance can be improved by allowing uncertainty in the specification of predictor relationships in this model. The methods are illustrated through simulation studies and analysis of an epidemiologic data set.

AB - Stochastic search variable selection (SSVS) algorithms provide an appealing and widely used approach for searching for good subsets of predictors while simultaneously estimating posterior model probabilities and model-averaged predictive distributions. This article proposes a two-level generalization of SSVS to account for missing predictors while accommodating uncertainty in the relationships between these predictors. Bayesian approaches for allowing predictors that are missing at random require a model on the joint distribution of the predictors. We show that predictive performance can be improved by allowing uncertainty in the specification of predictor relationships in this model. The methods are illustrated through simulation studies and analysis of an epidemiologic data set.

KW - missing at random

KW - model averaging

KW - multiple imputation

KW - stochastic search

KW - subset

KW - variable selection

U2 - 10.2202/1557-4679.1173

DO - 10.2202/1557-4679.1173

M3 - Journal article

VL - 6

JO - International Journal of Biostatistics

JF - International Journal of Biostatistics

IS - 1

ER -