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Fourier transform approach for inverse dimension reduction method.
- Source :
-
Journal of Nonparametric Statistics . Dec2018, Vol. 30 Issue 4, p1049-1071. 23p. - Publication Year :
- 2018
-
Abstract
- Estimating an inverse regression space is especially important in sufficient dimension reduction. However, it typically requires a tuning parameter, such as the number of slices in a slicing method or bandwidth selection in a kernel estimation approach. Such a requirement not only affects the accuracy of estimates in a finite sample, but also increases difficulties for multivariate models. In this paper, we use a Fourier transform approach to avoid such difficulties and incorporate multivariate models. We further develop a Fourier transform approach to deal with variable selection, categorical predictor variables, and large p, small n data. To test the dimension, asymptotic results are obtained. Simulation studies and data analysis show the efficacy of our proposed methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- *FOURIER transforms
*LEAST squares
*BANDWIDTHS
*MACHINE learning
*RANDOM variables
Subjects
Details
- Language :
- English
- ISSN :
- 10485252
- Volume :
- 30
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Journal of Nonparametric Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 132794362
- Full Text :
- https://doi.org/10.1080/10485252.2018.1515432