Sparse partial least squares regression for simultaneous dimension reduction and variable selection
نویسندگان
چکیده
منابع مشابه
Sparse partial least squares regression for simultaneous dimension reduction and variable selection
Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very lar...
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The reduced-rank regression is an effective method to predict multiple response variables from the same set of predictor variables, because it can reduce the number of model parameters as well as take advantage of interrelations between the response variables and therefore improve predictive accuracy. We propose to add a new feature to the reduced-rank regression that allows selection of releva...
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0167-8655/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.patrec.2011.11.009 ⇑ Corresponding author. Address: Department of C Tongji University, Cao’an Road 4800, Shanghai 2018 3706; fax: +86 21 6958 9241. E-mail address: [email protected] (M. You). Partial least squares (PLS) has been widely applied to process scientific data sets as an effective dimension reduction technique. The main...
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ژورنال
عنوان ژورنال: Journal of the Royal Statistical Society: Series B (Statistical Methodology)
سال: 2010
ISSN: 1369-7412,1467-9868
DOI: 10.1111/j.1467-9868.2009.00723.x