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3.10 文献笔记

原文 The Elements of Statistical Learning
翻译 szcf-weiya
时间 2017-09-09

线性回归在很多统计教材中都有讨论,比如,Seber (1984)1, Weisberg (1980)2 以及 Mardia et al. (1979)3。岭回归由 Hoerl and Kennard (1970)4提出,而 lasso 由Tibshirani (1996)5提出。几乎在同时,lasso形式的惩罚在信号处理中的 basis pursuit 方法中被提出(Chen et al., 1998)6。最小角回归过程由 Efron et al. (2004)7等人提出;与这有关的是早期 Osborne et al. (2000a)8和 Osborne et al. (2000b)9的homotopy过程。他们的算法也利用了在 LAR/lasso 算法中的分段线性,但是缺少透明度 (transparency)。向前逐步准则在 Hastie et al. (2007)10中进行了讨论。Park and Hastie (2007)11 发展了类似用于广义回归模型的最小角回归的路径算法。偏最小二乘由 Wold (1975)12提出。收缩方法的比较或许可以在 Copas (1983)13 和 Frank and Friedman (1993)14中找到。

weiya注

3.8节讲lasso及相关的路径算法一节中还有很多文献。


  1. Seber, G. (1984). Multivariate Observations, Wiley, New York. 

  2. Weisberg, S. (1980). Applied Linear Regression, Wiley, New York. 

  3. Mardia, K., Kent, J. and Bibby, J. (1979). Multivariate Analysis, Academic Press. 

  4. Hoerl, A. E. and Kennard, R. (1970). Ridge regression: biased estimation for nonorthogonal problems, Technometrics 12: 55–67. 

  5. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society, Series B 58: 267–288. 

  6. Chen, S. S., Donoho, D. and Saunders, M. (1998). Atomic decomposition by basis pursuit, SIAM Journal on Scientific Computing 20(1): 33–61. 

  7. Efron, B., Hastie, T., Johnstone, I. and Tibshirani, R. (2004). Least angle regression (with discussion), Annals of Statistics 32(2): 407–499. 

  8. Osborne, M., Presnell, B. and Turlach, B. (2000a). A new approach to variable selection in least squares problems, IMA Journal of Numerical Analysis 20: 389–404. 

  9. Osborne, M., Presnell, B. and Turlach, B. (2000b). On the lasso and its dual, Journal of Computational and Graphical Statistics 9: 319–337. 

  10. Hastie, T., Taylor, J., Tibshirani, R. and Walther, G. (2007). Forward stagewise regression and the monotone lasso, Electronic Journal of Statistics 1: 1–29. 

  11. Park, M. Y. and Hastie, T. (2007). l 1 -regularization path algorithm for generalized linear models, Journal of the Royal Statistical Society Series B 69: 659–677. 

  12. Wold, H. (1975). Soft modelling by latent variables: the nonlinear iterative partial least squares (NIPALS) approach, Perspectives in Probability and Statistics, In Honor of M. S. Bartlett, pp. 117–144. 

  13. Copas, J. B. (1983). Regression, prediction and shrinkage (with discussion), Journal of the Royal Statistical Society, Series B, Methodological 45: 311–354. 

  14. Frank, I. and Friedman, J. (1993). A statistical view of some chemometrics regression tools (with discussion), Technometrics 35(2): 109–148. 

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