Locally Sparse Estimator for Functional Linear Regression Models
نویسندگان
چکیده
منابع مشابه
Locally Sparse Estimator for Functional Linear Regression Models
A new locally sparse (i.e., zero on some subregions) estimator for coefficient functions in functional linear regression models is developed based on a novel functional regularization technique called “fSCAD”. The nice shrinkage property of fSCAD allows the proposed estimator to locate null subregions of coefficient functions without over shrinking non-zero values of coefficient functions. Addi...
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Zhenhua Lin1, Jiguo Cao2, Liangliang Wang3 and Haonan Wang4 1Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada. Email: [email protected] 2Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada. Email: [email protected] 3Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada. Email: l...
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ژورنال
عنوان ژورنال: Journal of Computational and Graphical Statistics
سال: 2017
ISSN: 1061-8600,1537-2715
DOI: 10.1080/10618600.2016.1195273