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On a new hybrid estimator for the central mean space

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Abstract

Existing estimators of the central mean space are known to have uneven performances across different types of link functions. By combining the strength of the ordinary least squares and the principal Hessian directions, the authors propose a new hybrid estimator that successfully recovers the central mean space for a wide range of link functions. Based on the new hybrid estimator, the authors further study the order determination procedure and the marginal coordinate test. The superior performance of the hybrid estimator over existing methods is demonstrated in extensive simulation studies.

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Authors and Affiliations

  1. Department of Statistical Science, Temple University, Philadelphia, USA

    Qi Xia & Yuexiao Dong

Authors
  1. Qi Xia
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  2. Yuexiao Dong
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Correspondence to Qi Xia.

Additional information

This paper was recommended for publication by Editor SHAO Jun.

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Xia, Q., Dong, Y. On a new hybrid estimator for the central mean space. J Syst Sci Complex 30, 111–121 (2017). https://doi.org/10.1007/s11424-017-6227-0

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  • DOI: https://doi.org/10.1007/s11424-017-6227-0

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