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Method-Free Permutation Predictor Hypothesis Tests in Sufficient Dimension Reduction
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 Title & Authors
Method-Free Permutation Predictor Hypothesis Tests in Sufficient Dimension Reduction
Lee, Kyungjin; Oh, Suji; Yoo, Jae Keun;
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 Abstract
In this paper, we propose method-free permutation predictor hypothesis tests in the context of sufficient dimension reduction. Different from an existing method-free bootstrap approach, predictor hypotheses are evaluated based on p-values; therefore, usual statistical practitioners should have a potential preference. Numerical studies validate the developed theories, and real data application is provided.
 Keywords
Permutation;predictor hypothesis tests;regression;sufficient dimension reduction;
 Language
English
 Cited by
 References
1.
Cook, R. D. (1998a). Principal Hessian directions revisited, Journal of the American Statistical Association, 93, 84-100. crossref(new window)

2.
Cook, R. D. (1998b). Regression Graphics, Wiley, New York.

3.
Cook, R. D. (2004). Testing predictor contributions in sufficient dimension reduction, Annals of Statistics, 32, 1062-1092. crossref(new window)

4.
Cook, R. D. and Li, B. (2002). Dimension reduction for the conditional mean, Annals of Statistics, 30, 455-474. crossref(new window)

5.
Cook, R. D. andWeisberg, S. (1991). Discussion of sliced inverse regression for dimension reduction by K.C. Li, Journal of the American Statistical Association, 86, 328-332.

6.
Hotelling, H. (1936). Relations between two sets of variates, Biometrika, 28, 321-377. crossref(new window)

7.
Li, K. C. (1991). Sliced inverse regression for dimension reduction, Journal of the American Statistical Association, 86, 326-342.

8.
Li, K. C. (1992). On principal Hessian directions for data visualization and dimension reduction: Another application of Stein's lemma, Journal of the American Statistical Association, 87, 1025-1039. crossref(new window)

9.
Shao, Y., Cook, R. D. and Weisberg, S. (2007). Marginal tests of sliced average variance estimation, Biometrika, 94, 285-296. crossref(new window)

10.
Ye, Z. andWeiss R. E. (2003). Using the bootstrap to select one of a new class of dimension reduction methods, Journal of the American Statistical Association, 98, 968-979. crossref(new window)

11.
Yin, X. and Cook, R. D. (2002). Dimension reduction for the conditional kth moment in regression. Journal of Royal Statistical Society, Series B, 64, 159-175. crossref(new window)

12.
Yoo, J. K. (2011). Unified predictor hypothesis tests in sufficient dimension reduction: Bootstrap approach, Journal of the Korean Statistical Society, 40, 217-222 crossref(new window)