On kernel method for sliced average variance estimation

Li-Ping Zhu and Li-Xing Zhu

Journal of Multivariate Analysis, 2007, vol. 98, issue 5, 970-991

Abstract: In this paper, we use the kernel method to estimate sliced average variance estimation (SAVE) and prove that this estimator is both asymptotically normal and root n consistent. We use this kernel estimator to provide more insight about the differences between slicing estimation and other sophisticated local smoothing methods. Finally, we suggest a Bayes information criterion (BIC) to estimate the dimensionality of SAVE. Examples and real data are presented for illustrating our method.

Keywords: Asymptotic; normality; Bandwidth; selection; Dimension; reduction; Kernel; estimation; Sliced; average; variance; estimation; Sliced; inverse; regression; Slicing; estimation (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (12)

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