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18.8 文献笔记

原文 The Elements of Statistical Learning
翻译 szcf-weiya
发布 2017-06-09

许多文献已经在本章的特定地方给出来了；这里我们在列出另外的一些文献．Dudoit et al. (2002a)1给出了对基因表达数据的判别分析方法的概述及比较．Levina (2002)2 做了一些数学分析在$p, N\rightarrow \infty, p>N$的情况下比较对角LDA和全LDA．她证明了在合理的假设下，对角LDA比全LDA有更低的极限误差率．Tibshirani et al. (2001a)3和 Tibshirani et al. (2003)4 提出了最近收缩中心分类器．Zhu and Hastie (2004)5研究了正则化逻辑斯蒂回归．高维回归和lasso是非常活跃的研究领域，许多的文献在3.8.5节给出．Tibshirani et al. (2005)6提出fused lasso，而Zou and Hastie (2005)7提出弹性网．Bair and Tibshirani (2004)8和 Bair et al. (2006)9 中讨论了有监督的主成分．关于censored survival data分析的介绍，参见Kalbfleisch and Prentice (1980)10．

微阵列技术导致了一系列的统计研究：参见Speed (2003)11、Parmigiani et al. (2003)12、Simon et al. (2004)13以及Lee (2004)14等书中的例子．

错误发现率由Benjamini and Hochberg (1995)15提出，并且被这些作者以及其他作者进行研究和推广．FDR部分的文章或许可以在Yoav Benjamini的主页中找到．一些最近的文章包括Efron and Tibshirani (2002)16, Storey (2002)17, Genovese and Wasserman (2004)18, Storey and Tibshirani (2003)19 以及 Benjamini and Yekutieli (2005)20．Dudoit et al. (2002b)21对微阵列研究中的差异表达基因的识别的方法进行了综述．

  1. Dudoit, S., Fridlyand, J. and Speed, T. (2002a). Comparison of discrimination methods for the classification of tumors using gene expression data, Journal of the American Statistical Association 97(457): 77–87. ↩

  2. Levina, E. (2002). Statistical issues in texture analysis, PhD thesis, Department. of Statistics, University of California, Berkeley. ↩

  3. Tibshirani, R., Hastie, T., Narasimhan, B. and Chu, G. (2001a). Diagnosis of multiple cancer types by shrunken centroids of gene expression, Proceedings of the National Academy of Sciences 99: 6567–6572. ↩

  4. Tibshirani, R., Hastie, T., Narasimhan, B. and Chu, G. (2003). Class prediction by nearest shrunken centroids, with applications to DNA microarrays, Statistical Science 18(1): 104–117. ↩

  5. Zhu, J. and Hastie, T. (2004). Classification of gene microarrays by penalized logistic regression, Biostatistics 5(2): 427–443. ↩

  6. Tibshirani, R., Saunders, M., Rosset, S., Zhu, J. and Knight, K. (2005). Sparsity and smoothness via the fused lasso, Journal of the Royal Statistical Society, Series B 67: 91–108. ↩

  7. Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society Series B. 67(2): 301–320. ↩

  8. Bair, E. and Tibshirani, R. (2004). Semi-supervised methods to predict patient survival from gene expression data, PLOS Biology 2: 511–522. ↩

  9. Bair, E., Hastie, T., Paul, D. and Tibshirani, R. (2006). Prediction by supervised principal components, Journal of the American Statistical Association 101: 119–137. ↩

  10. Kalbfleisch, J. and Prentice, R. (1980). The Statistical Analysis of Failure Time Data, Wiley, New York. ↩

  11. Speed, T. (ed.) (2003). Statistical Analysis of Gene Expression Microarray Data, Chapman and Hall, London. ↩

  12. Parmigiani, G., Garett, E. S., Irizarry, R. A. and Zeger, S. L. (eds) (2003). The Analysis of Gene Expression Data, Springer, New York. ↩

  13. Simon, R. M., Korn, E. L., McShane, L. M., Radmacher, M. D., Wright, G. and Zhao, Y. (2004). Design and Analysis of DNA Microarray Investigations, Springer, New York. ↩

  14. Lee, M.-L. (2004). Analysis of Microarray Gene Expression Data, Kluwer Academic Publishers. ↩

  15. Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B. 85: 289–300. ↩

  16. Efron, B. and Tibshirani, R. (2002). Microarrays, empirical Bayes methods, and false discovery rates, Genetic Epidemiology 1: 70–86. ↩

  17. Storey, J. (2002). A direct approach to false discovery rates, Journal of the Royal Statistical Society B. 64(3): 479–498. ↩

  18. Genovese, C. and Wasserman, L. (2004). A stochastic process approach to false discovery rates, Annals of Statistics 32(3): 1035–1061. ↩

  19. Storey, J. and Tibshirani, R. (2003). Statistical significance for genomewide studies, Proceedings of the National Academy of Sciences 100-: 9440– 9445. ↩

  20. Benjamini, Y. and Yekutieli, Y. (2005). False discovery rate controlling confidence intervals for selected parameters, Journal of the American Statistical Association 100: 71–80. ↩

  21. Dudoit, S., Yang, Y., Callow, M. and Speed, T. (2002b). Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments, Statistica Sinica pp. 111–139. ↩

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