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Bayesian multiple response kernel regression model for high dimensional data and its practical applications in near infrared spectroscopy

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  • Chakraborty, Sounak
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    Non-linear regression based on reproducing kernel Hilbert space (RKHS) has recently become very popular in fitting high-dimensional data. The RKHS formulation provides an automatic dimension reduction of the covariates. This is particularly helpful when the number of covariates (p) far exceed the number of data points. In this paper, we introduce a Bayesian nonlinear multivariate regression model for high-dimensional problems. Our model is suitable when we have multiple correlated observed response corresponding to same set of covariates. We introduce a robust Bayesian support vector regression model based on a multivariate version of Vapnik’s ϵ-insensitive loss function. The likelihood corresponding to the multivariate Vapnik’s ϵ-insensitive loss function is constructed as a scale mixture of truncated normal and gamma distribution. The regression function is constructed using the finite representation of a function in the reproducing kernel Hilbert space (RKHS). The kernel parameter is estimated adaptively by assigning a prior on it and using the Markov chain Monte Carlo (MCMC) techniques for computation.

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    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 56 (2012)
    Issue (Month): 9 ()
    Pages: 2742-2755

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    Handle: RePEc:eee:csdana:v:56:y:2012:i:9:p:2742-2755
    DOI: 10.1016/j.csda.2012.02.019
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    1. Smith, Michael & Kohn, Robert, 2000. "Nonparametric seemingly unrelated regression," Journal of Econometrics, Elsevier, vol. 98(2), pages 257-281, October.
    2. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    3. Chakraborty, Sounak & Ghosh, Malay & Mallick, Bani K., 2012. "Bayesian nonlinear regression for large p small n problems," Journal of Multivariate Analysis, Elsevier, vol. 108(C), pages 28-40.
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