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On model-free conditional coordinate tests for regressions

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Author Info

  • Yu, Zhou
  • Zhu, Lixing
  • Wen, Xuerong Meggie

Abstract

Existing model-free tests of the conditional coordinate hypothesis in sufficient dimension reduction (Cook (1998) [3]) focused mainly on the first-order estimation methods such as the sliced inverse regression estimation (Li (1991) [14]). Such testing procedures based on quadratic inference functions are difficult to be extended to second-order sufficient dimension reduction methods such as the sliced average variance estimation (Cook and Weisberg (1991) [9]). In this article, we develop two new model-free tests of the conditional predictor hypothesis. Moreover, our proposed test statistics can be adapted to commonly used sufficient dimension reduction methods of eigendecomposition type. We derive the asymptotic null distributions of the two test statistics and conduct simulation studies to examine the performances of the tests.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Multivariate Analysis.

Volume (Year): 109 (2012)
Issue (Month): C ()
Pages: 61-72

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Handle: RePEc:eee:jmvana:v:109:y:2012:i:c:p:61-72

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Related research

Keywords: Conditional coordinate test; Sufficient dimension reduction; Sliced inverse regression;

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Cited by:
  1. repec:wyi:journl:002176 is not listed on IDEAS
  2. Yu, Zhou & Dong, Yuexiao & Guo, Ranwei, 2013. "On determining the structural dimension via directional regression," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 987-992.

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