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Sparse Bayesian variable selection in kernel probit model for analyzing high-dimensional data

Author

Listed:
  • Aijun Yang

    (Nanjing Forestry University)

  • Yuzhu Tian

    (Henan University of Science and Technology)

  • Yunxian Li

    (Yunnan University of Finance and Economics)

  • Jinguan Lin

    (Nanjing Audit University)

Abstract

In this paper, we developed a sparse Bayesian variable selection in kernel probit model for high-dimensional data classification. Particularly we assigned a correlation prior distribution on the model size and a sparse prior distribution on the regression parameters. MCMC-based computation algorithms are outlined to generate samples from the posterior distributions. Simulation and real data studies show that in terms of the accuracy of variable selection and classification, our proposed method performs better than the other five Bayesian methods without the correlation term in the prior or those involving only one shrinkage parameter.

Suggested Citation

  • Aijun Yang & Yuzhu Tian & Yunxian Li & Jinguan Lin, 2020. "Sparse Bayesian variable selection in kernel probit model for analyzing high-dimensional data," Computational Statistics, Springer, vol. 35(1), pages 245-258, March.
    • Handle: RePEc:spr:compst:v:35:y:2020:i:1:d:10.1007_s00180-019-00917-8
    DOI: 10.1007/s00180-019-00917-8
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    References listed on IDEAS

    as
    1. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    2. Aijun Yang & Ju Xiang & Lianjie Shu & Hongqiang Yang, 2018. "Sparse Bayesian Variable Selection with Correlation Prior for Forecasting Macroeconomic Variable using Highly Correlated Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 51(2), pages 323-338, February.
    3. Yang Aijun & Xiang Ju & Yang Hongqiang & Lin Jinguan, 2018. "Sparse Bayesian Variable Selection in Probit Model for Forecasting U.S. Recessions Using a Large Set of Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 1123-1138, April.
    4. Panagiotelis, Anastasios & Smith, Michael, 2008. "Bayesian identification, selection and estimation of semiparametric functions in high-dimensional additive models," Journal of Econometrics, Elsevier, vol. 143(2), pages 291-316, April.
    5. Yuan, Ming & Lin, Yi, 2005. "Efficient Empirical Bayes Variable Selection and Estimation in Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1215-1225, December.
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    Cited by:

    1. Rajput, Sheraz Mustafa & Javaid, Muhammad Nadeem & Junaid, Ahmad, 2023. "Financial Development and Income Inequality: A U-shaped Relationship," Asian Journal of Applied Economics, Kasetsart University, Center for Applied Economics Research, vol. 30(2), July.

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