基于广义加乘危险率模型的多元数据病例队列试验研究，郑明，孙怡，在大型流行病学跟踪研究中,病例队列抽样设计是一种常用的降低抽样成本的抽样方法。在这种抽样设计中,只要求观测所有病例样本以及�

Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners. It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added. All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior. All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate Statistical Analysis offers the following new features: A new chapter on Variable Selection (Lasso, SCAD and Elastic Net) All exercises are supplemented by R and MATLAB code that can be found on www.quantlet.de. The practical exercises include solutions that can be found in Härdle, W. and Hlavka, Z., Multivariate Statistics: Exercises and Solutions. Springer Verlag, Heidelberg. Table of Contents Part I Descriptive Techniques Chapter 1 Comparison of Batches Part II Multivariate Random Variables Chapter 2 A Short Excursion into Matrix Algebra Chapter 3 Moving to Higher Dimensions Chapter 4 Multivariate Distributions Chapter 5 Theory of the Multinormal Chapter 6 Theory of Estimation Chapter 7 Hypothesis Testing Part III Multivariate Techniques Chapter 8 Regression Models Chapter 9 Variable Selection Chapter 10 Decomposition of Data Matrices by Factors Chapter 11 Principal Components Analysis Chapter 12 Factor Analysis Chapter 13 Cluster Analysis Chapter 14 Discriminant Analysis Chapter 15 Correspondence Analysis Chapter 16 Canonical Correlation Analysis Chapter 17 Multidimensional Scaling Chapter 18 Conjoint Measurement Analysis Chapter 19 Applications in Finance Chapter 20 Computationally Intensive Techniques Part IV Appendix Chapter 21 Symbols and Notations Chapter 22 Data

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Brodley.Multivariate DecisionTrees1995多变量决策树1

经典教材Aspects of multivariate statistical theory工程技术人员必备工具书。

多元时间序列 (MTS) 数据集广泛存在于众多领域，包括医疗保健、多媒体、金融和生物识别。 由于MTS是许多计算机视觉和模式识别应用中的重要元素，因此如何准确地对MTS进行分类已成为研究的热点。 在代码中，我们为 MTS 分类提出了基于马氏距离的动态时间规整 (MDDTW) 度量。 Mahalanobis 距离在每个变量与其对应的类别之间建立了准确的关系。 它用于计算 MTS 中​​向量之间的局部距离。 然后我们使用动态时间扭曲 (DTW) 来对齐那些不同步或长度不同的 MTS。 同时，我们使用基于 LogDet 散度的三元组约束（LDMLT）模型来学习具有高精度和鲁棒性的 Mahalanobis 矩阵。 此外，我们还演示了代码在 MTS 数据“JapaneseVowels”上的性能。

科学用实验来验证关于世界的假设。统计学提供了量化这一过程的工具，并提供了将数据(实验)与概率模型(假设)联系起来的方法。因为世界是复杂的，我们需要复杂的模型和复杂的数据，因此需要多元统计和机器学习。具体来说，多元统计(与单变量统计相反)涉及随机向量和随机矩阵的方法和模型，而不仅仅是随机单变量(标量)变量。因此，在多元统计中，我们经常使用矩阵表示法。与多元统计(传统统计学的一个分支)密切相关的是机器学习(ML)，它传统上是计算机科学的一个分支。过去机器学习主要集中在算法上，而不是概率建模，但现在大多数机器学习方法都完全基于统计多元方法，因此这两个领域正在收敛。多变量模型提供了一种方法来学习随机变量组成部分之间的依赖关系和相互作用，这反过来使我们能够得出有关兴趣的潜在机制的结论(如生物或医学)。 两个主要任务: 无监督学习(寻找结构，聚类) 监督学习(从标记数据进行训练，然后进行预测) 挑战: 模型的复杂性需要适合问题和可用数据， 高维使估计和推断困难 计算问题。

多元统计分析的绝佳教材，第三版了，斯坦福大学Aderson经典之作

《Applied Multivariate Statistical Analysis》pdf, Wolfgang, 4th edition, 英文版

Perfected over three editions and more than forty years, this field- and classroom-tested reference: 　　* Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures. 　　* Treats all the basic and important topics in multivariate statistics. 　　* Adds two new chapters, along with a number of new sections. 　　* Provides the most methodical, up-to-date information on MV statistics available.