Please use this identifier to cite or link to this item: https://doi.org/10.1111/j.1467-9868.2007.00647.x
Title: A semiparametric approach to canonical analysis
Authors: Xia, Y. 
Keywords: Canonical correlation analysis
Consistency
Cross-validation
Kernel smoothing
Non-linear time series
Single-index model
Issue Date: Jul-2008
Citation: Xia, Y. (2008-07). A semiparametric approach to canonical analysis. Journal of the Royal Statistical Society. Series B: Statistical Methodology 70 (3) : 519-543. ScholarBank@NUS Repository. https://doi.org/10.1111/j.1467-9868.2007.00647.x
Abstract: Classical canonical correlation analysis is one of the fundamental tools in statistics to investigate the linear association between two sets of variables. We propose a method, called semiparametric canonical analysis, to generalize canonical correlation analysis to incorporate the important non-linear association. Semiparametric canonical analysis is easy to implement and interpret. Statistical properties are proved. A consistent estimation method is developed. Selection of significant semiparametric canonical analysis components is discussed. Simulations suggest that the methods proposed have satisfactory performance in finite samples. One environmental data set and one data set in social science are investigated, in which non-linear canonical associations are observed and interpreted. © 2008 Royal Statistical Society.
Source Title: Journal of the Royal Statistical Society. Series B: Statistical Methodology
URI: http://scholarbank.nus.edu.sg/handle/10635/104967
ISSN: 13697412
DOI: 10.1111/j.1467-9868.2007.00647.x
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