Functional k-means inverse regression

Guochang Wang, Nan Lin and Baoxue Zhang

Computational Statistics & Data Analysis, 2014, vol. 70, issue C, 172-182

Abstract: A new dimension reduction method is proposed for functional multivariate regression with a multivariate response and a functional predictor by extending the functional sliced inverse regression model. Naive application of existing dimension reduction techniques for univariate response will create too many hyper-rectangular slices. To avoid this curse of dimensionality, a new slicing method is proposed by clustering over the space of the multivariate response, which generates a much smaller set of slices of flexible shapes. The proposed method can be applied to any number of response variables and can be particularly useful for exploratory analysis. In addition, a new eigenvalue-based method for determining the dimensionality of the reduced space is developed. Real and simulation data examples are then presented to demonstrate the effectiveness of the proposed method.

Keywords: Dimension reduction; Effective direction reduction; Functional data; Multivariate regression; k-means clustering (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:70:y:2014:i:c:p:172-182

DOI: 10.1016/j.csda.2013.09.004

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