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Group-wise semiparametric modeling: A SCSE approach

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  • Song, Song
  • Zhu, Lixing

Abstract

This paper is motivated by the modeling of a high-dimensional dataset via group-wise information on explanatory variables. A three-step algorithm is suggested for group-wise semiparametric modeling: (i) screening to reduce dimensionality; (ii) clustering according to grouped explanatory variables; (iii) sign-constraints-based estimation for coefficients to produce meaningful interpretations. As a justification, under the setup of m-dependent and β-mixing processes, the interplay between the estimator’s convergence rate and the temporal dependence level is quantified and a cross-validation result about the resampling scheme for threshold selection is also proved. This method is evaluated in finite-sample cases through a Monte Carlo experiment, and illustrated with an analysis of the US consumer price index.

Suggested Citation

  • Song, Song & Zhu, Lixing, 2016. "Group-wise semiparametric modeling: A SCSE approach," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 1-14.
    • Handle: RePEc:eee:jmvana:v:152:y:2016:i:c:p:1-14
    DOI: 10.1016/j.jmva.2016.07.006
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    References listed on IDEAS

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    5. Song, Song & Bickel, Peter J., 2011. "Large vector auto regressions," SFB 649 Discussion Papers 2011-048, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. Li, Lexin & Li, Bing & Zhu, Li-Xing, 2010. "Groupwise Dimension Reduction," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1188-1201.
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