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On testing common indices for two multi-index models: A link-free approach

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  • Liu, Xuejing
  • Yu, Zhou
  • Wen, Xuerong Meggie
  • Paige, Robert

Abstract

We propose a link-free procedure for testing whether two multi-index models share identical indices via the sufficient dimension reduction approach. Test statistics are developed based upon three different sufficient dimension reduction methods: (i) sliced inverse regression, (ii) sliced average variance estimation and (iii) directional regression. The asymptotic null distributions of our test statistics are derived. Monte Carlo studies are performed to investigate the efficacy of our proposed methods. A real-world application is also considered.

Suggested Citation

  • Liu, Xuejing & Yu, Zhou & Wen, Xuerong Meggie & Paige, Robert, 2015. "On testing common indices for two multi-index models: A link-free approach," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 75-85.
    • Handle: RePEc:eee:jmvana:v:136:y:2015:i:c:p:75-85
    DOI: 10.1016/j.jmva.2015.01.009
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    References listed on IDEAS

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    Cited by:

    1. Wei Luo & Yeying Zhu & Debashis Ghosh, 2017. "On estimating regression-based causal effects using sufficient dimension reduction," Biometrika, Biometrika Trust, vol. 104(1), pages 51-65.
    2. Liu, Xuejing & Huo, Lei & Wen, Xuerong Meggie & Paige, Robert, 2017. "A link-free approach for testing common indices for three or more multi-index models," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 236-245.

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