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Gcvpack – routines for generalized cross validation

 

作者: Douglas M. Bates,   Mary J Lindstrom,   Grace Wahba,   Brian S Yandell,  

 

期刊: Communications in Statistics - Simulation and Computation  (Taylor Available online 1987)
卷期: Volume 16, issue 1  

页码: 263-297

 

ISSN:0361-0918

 

年代: 1987

 

DOI:10.1080/03610918708812590

 

出版商: Marcel Dekker, Inc.

 

关键词: Ill-posed problems;partial thin plate smoothing splines;penalized likelihood;semi-parametric models;ridge regression;thin plate smoothing splines;truncated singular value decomposition

 

数据来源: Taylor

 

摘要:

These Fortran-77 subroutines provide building blocks for Generalized Cross-Validation (GCV) (Craven and Wahba, 1979) calculations in data analysis and data smoothing including ridge regression (Golub, Heath, and Wahba, 1979), thin plate smoothing splines (Wahba and Wendelberger, 1980), deconvolution (Wahba, 1982d), smoothing of generalized linear models (O'sullivan, Yandell and Raynor 1986, Green 1984 and Green and Yandell 1985), and ill-posed problems (Nychka et al., 1984, O'sullivan and Wahba, 1985). We present some of the types of problems for which GCV is a useful method of choosing a smoothing or regularization parameter and we describe the structure of the subroutines.Ridge Regression: A familiar example of a smoothing parameter is the ridge parameter X in the ridge regression problem which we write.

 

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