An approach to estimating nonlinear sufficient dimension  reduction subspace for censored survival data

doi:10.3969/j.issn.0253-2778.2015.09.001

Journal of University of Science and Technology of China ›› 2015, Vol. 45 ›› Issue (9): 709-716.DOI: 10.3969/j.issn.0253-2778.2015.09.001

• Original Paper •

An approach to estimating nonlinear sufficient dimension reduction subspace for censored survival data

CUI Wenquan, WU Chenglong

Department of Statistics and Finance, School of Management, University of Science and of Technology of China,Hefei 230026, China

Received:2015-03-03 Revised:2015-05-21 Accepted:2015-05-21 Online:2015-05-21 Published:2015-05-21

Abstract

Abstract: An approach was proposed to estimating the nonlinear sufficient dimension reduction (SDR) subspace for survival data with censorship. Based on the theory of reproducing kernel Hilbert spaces (RKHS) and the double slicing procedure，the joint nonlinear sufficient dimension reduction central subspace was estimated by means of the generalized eigen-decomposition equation. And the weight function was estimated by the definition and property of SDR central subspace. The efficiency was improved by the iteration method while the algorithm was being implemented. Finally， the performance of the proposed method was illustrated on simulated data.

Key words: RKHS, sufficient dimension reduction, sliced inverse regression, survival data

CLC Number:

O212

CUI Wenquan, WU Chenglong. An approach to estimating nonlinear sufficient dimension reduction subspace for censored survival data[J]. Journal of University of Science and Technology of China, 2015, 45(9): 709-716.

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An approach to estimating nonlinear sufficient dimension reduction subspace for censored survival data

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