Unified predictor hypothesis tests in sufficient dimension reduction: A bootstrap approach

Jae Keun Yoo

doi:10.1016/j.jkss.2010.09.006

Unified predictor hypothesis tests in sufficient dimension reduction: A bootstrap approach

Jae Keun Yoo

Department of Statistics

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

In this paper, we newly define a unified predictor hypothesis that is applicable to all sufficient dimension reduction (SDR) methodologies. To test the predictor hypothesis, we propose a bootstrap approach by measuring the distances between reference subspaces and bootstrap subspaces. To measure the distances between two subspaces, the vector correlation coefficient is considered. Simulation studies confirm the background reasoning of the proposed tests.

Original language	English
Pages (from-to)	217-225
Number of pages	9
Journal	Journal of the Korean Statistical Society
Volume	40
Issue number	2
DOIs	https://doi.org/10.1016/j.jkss.2010.09.006
State	Published - Jun 2011

Bibliographical note

Funding Information:
For Jae Keun Yoo, this work was supported by a Basic Science Research Program through the National Research Foundation of Korea (KRF) funded by the Ministry of Education, Science and Technology ( 2010-0003189 ). The authors are also grateful to the referees for many helpful comments.

Keywords

Bootstrapping
Predictor hypothesis tests
Sufficient dimension reduction
Vector correlation coefficient

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10.1016/j.jkss.2010.09.006

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abstract = "In this paper, we newly define a unified predictor hypothesis that is applicable to all sufficient dimension reduction (SDR) methodologies. To test the predictor hypothesis, we propose a bootstrap approach by measuring the distances between reference subspaces and bootstrap subspaces. To measure the distances between two subspaces, the vector correlation coefficient is considered. Simulation studies confirm the background reasoning of the proposed tests.",

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note = "Funding Information: For Jae Keun Yoo, this work was supported by a Basic Science Research Program through the National Research Foundation of Korea (KRF) funded by the Ministry of Education, Science and Technology ( 2010-0003189 ). The authors are also grateful to the referees for many helpful comments.",

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PY - 2011/6

Y1 - 2011/6

N2 - In this paper, we newly define a unified predictor hypothesis that is applicable to all sufficient dimension reduction (SDR) methodologies. To test the predictor hypothesis, we propose a bootstrap approach by measuring the distances between reference subspaces and bootstrap subspaces. To measure the distances between two subspaces, the vector correlation coefficient is considered. Simulation studies confirm the background reasoning of the proposed tests.

AB - In this paper, we newly define a unified predictor hypothesis that is applicable to all sufficient dimension reduction (SDR) methodologies. To test the predictor hypothesis, we propose a bootstrap approach by measuring the distances between reference subspaces and bootstrap subspaces. To measure the distances between two subspaces, the vector correlation coefficient is considered. Simulation studies confirm the background reasoning of the proposed tests.

KW - Bootstrapping

KW - Predictor hypothesis tests

KW - Sufficient dimension reduction

KW - Vector correlation coefficient

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JF - Journal of the Korean Statistical Society

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ER -

Unified predictor hypothesis tests in sufficient dimension reduction: A bootstrap approach

Abstract

Bibliographical note

Keywords

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