Abstract
We present a novel approach to sufficient dimension reduction for the conditional kth moments in regression. The approach provides a computationally feasible test for the dimension of the central kth-moment subspace. In addition, we can test predictor effects without assuming any models. All test statistics proposed in the novel approach have asymptotic chi-squared distributions.
| Original language | English |
|---|---|
| Article number | A014 |
| Pages (from-to) | 191-201 |
| Number of pages | 11 |
| Journal | Journal of Statistical Computation and Simulation |
| Volume | 83 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2013 |
Bibliographical note
Funding Information:This work was supported by Basic Science Research Programme through the National Research Foundation of Korea (KRF) funded by the Ministry of Education, Science and Technology (2011-0005581). The author is grateful to the referees for many helpful comments.
Publisher Copyright:
© 2013 Taylor & Francis.
Keywords
- Chi-squared tests
- Kth-moment dimension reduction
- Predictor effect tests
- Regression
- Sufficient dimension reduction