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Comparing Stochastic Optimization Methods for Variable Selection in Binary Outcome Prediction, With Application to Health Policy

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  • Fouskakis, Dimitris
  • Draper, David

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  • Fouskakis, Dimitris & Draper, David, 2008. "Comparing Stochastic Optimization Methods for Variable Selection in Binary Outcome Prediction, With Application to Health Policy," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1367-1381.
    • Handle: RePEc:bes:jnlasa:v:103:i:484:y:2008:p:1367-1381
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    Cited by:

    1. D. Fouskakis & I. Ntzoufras & D. Draper, 2009. "Population‐based reversible jump Markov chain Monte Carlo methods for Bayesian variable selection and evaluation under cost limit restrictions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(3), pages 383-403, July.
    2. Fouskakis, D., 2012. "Bayesian variable selection in generalized linear models using a combination of stochastic optimization methods," European Journal of Operational Research, Elsevier, vol. 220(2), pages 414-422.
    3. P. Richard Hahn & Carlos M. Carvalho, 2015. "Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 435-448, March.
    4. Brusco, Michael J., 2014. "A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 38-53.
    5. Paz, Alexander & Arteaga, Cristian & Cobos, Carlos, 2019. "Specification of mixed logit models assisted by an optimization framework," Journal of choice modelling, Elsevier, vol. 30(C), pages 50-60.

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