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.　　

MATLAB格式的几个常用多变量时序数据集，可以用于分类或者聚类研究。包括ArabicDigits、AUSLAN、CharacterTrajectories、CMUsubject16、ECG、JapaneseVowels、KickvsPunch、Libras、NetFlow、UWave、Wafer、WalkvsRun。数据集来自Mustafa Gokce Baydogan. Multivariate Time Series Classification Datasets.

1 Introduction 1 1.1 Classical and robust approaches to statistics 1 1.2 Mean and standard deviation 2 1.3 The “three-sigma edit” rule 5 1.4 Linear regression 7 1.4.1 Straight-line regression 7 1.4.2 Multiple linear regression 9 1.5 Correlation coefficients 11 1.6 Other parametric models 13 1.7 Problems 15 2 Location and Scale 17 2.1 The location model 17 2.2 M-estimates of location 22 2.2.1 Generalizing maximum likelihood 22 2.2.2 The distribution of M-estimates 25 2.2.3 An intuitive view of M-estimates 27 2.2.4 Redescending M-estimates 29 2.3 Trimmed means 31 2.4 Dispersion estimates 32 2.5 M-estimates of scale 34 2.6 M-estimates of location with unknown dispersion 36 2.6.1 Previous estimation of dispersion 37 2.6.2 Simultaneous M-estimates of location and dispersion 37 2.7 Numerical computation of M-estimates 39 2.7.1 Location with previously computed dispersion estimation 39 2.7.2 Scale estimates 40 2.7.3 Simultaneous estimation of location and dispersion 41 viii CONTENTS 2.8 Robust confidence intervals and tests 41 2.8.1 Confidence intervals 41 2.8.2 Tests 43 2.9 Appendix: proofs and complements 44 2.9.1 Mixtures 44 2.9.2 Asymptotic normality of M-estimates 45 2.9.3 Slutsky’s lemma 46 2.9.4 Quantiles 46 2.9.5 Alternative algorithms for M-estimates 46 2.10 Problems 48 3 Measuring Robustness 51 3.1 The influence function 55 3.1.1 *The convergence of the SC to the IF 57 3.2 The breakdown point 58 3.2.1 Location M-estimates 58 3.2.2 Scale and dispersion estimates 59 3.2.3 Location with previously computed dispersion estimate 60 3.2.4 Simultaneous estimation 60 3.2.5 Finite-sample breakdown point 61 3.3 Maximum asymptotic bias 62 3.4 Balancing robustness and efficiency 64 3.5 *“Optimal” robustness 65 3.5.1 Bias and variance optimality of location estimates 66 3.5.2 Bias optimality of scale and dispersion estimates 66 3.5.3 The infinitesimal approach 67 3.5.4 The Hampel approach 68 3.5.5 Balancing bias and variance: the general problem 70 3.6 Multidimensional parameters 70 3.7 *Estimates as functionals 71 3.8 Appendix: proofs of results 75 3.8.1 IF of general M-estimates 75 3.8.2 Maximum BP of location estimates 76 3.8.3 BP of location M-estimates 76 3.8.4 Maximum bias of location M-estimates 78 3.8.5 The minimax bias property of the median 79 3.8.6 Minimizing the GES 80 3.8.7 Hampel optimality 82 3.9 Problems 84 4 Linear Regression 1 87 4.1 Introduction 87 4.2 Review of the LS method 91 4.3 Classical methods for outlier detection 94 CONTENTS ix 4.4 Regression M-estimates 98 4.4.1 M-estimates with known scale 99 4.4.2 M-estimates with preliminary scale 100 4.4.3 Simultaneous estimation of regression and scale 103 4.5 Numerical computation of monotone M-estimates 103 4.5.1 The L1 estimate 103 4.5.2 M-estimates with smooth ψ-function 104 4.6 Breakdown point of monotone regression estimates 105 4.7 Robust tests for linear hypothesis 107 4.7.1 Review of the classical theory 107 4.7.2 Robust tests using M-estimates 108 4.8 *Regression quantiles 110 4.9 Appendix: proofs and complements 110 4.9.1 Why equivariance? 110 4.9.2 Consistency of estimated slopes under asymmetric errors 111 4.9.3 Maximum FBP of equivariant estimates 112 4.9.4 The FBP of monotone M-estimates 113 4.10 Problems 114 5 Linear Regression 2 115 5.1 Introduction 115 5.2 The linear model with random predictors 118 5.3 M-estimates with a bounded ρ-function 119 5.4 Properties of M-estimates with a bounded ρ-function 120 5.4.1 Breakdown point 122 5.4.2 Influence function 123 5.4.3 Asymptotic normality 123 5.5 MM-estimates 124 5.6 Estimates based on a robust residual scale 126 5.6.1 S-estimates 129 5.6.2 L-estimates of scale and the LTS estimate 131 5.6.3 Improving efficiency with one-step reweighting 132 5.6.4 A fully efficient one-step procedure 133 5.7 Numerical computation of estimates based on robust scales 134 5.7.1 Finding local minima 136 5.7.2 The subsampling algorithm 136 5.7.3 A strategy for fast iterative estimates 138 5.8 Robust confidence intervals and tests for M-estimates 139 5.8.1 Bootstrap robust confidence intervals and tests 141 5.9 Balancing robustness and efficiency 141 5.9.1 “Optimal” redescending M-estimates 144 5.10 The exact fit property 146 5.11 Generalized M-estimates 147 5.12 Selection of variables 150 x CONTENTS 5.13 Heteroskedastic errors 153 5.13.1 Improving the efficiency of M-estimates 153 5.13.2 Estimating the asymptotic covariance matrix under heteroskedastic errors 154 5.14 *Other estimates 156 5.14.1 τ -estimates 156 5.14.2 Projection estimates 157 5.14.3 Constrained M-estimates 158 5.14.4 Maximum depth estimates 158 5.15 Models with numeric and categorical predictors 159 5.16 *Appendix: proofs and complements 162 5.16.1 The BP of monotone M-estimates with random X 162 5.16.2 Heavy-tailed x 162 5.16.3 Proof of the exact fit property 163 5.16.4 The BP of S-estimates 163 5.16.5 Asymptotic bias of M-estimates 166 5.16.6 Hampel optimality for GM-estimates 167 5.16.7 Justification of RFPE* 168 5.16.8 A robust multiple correlation coefficient 170 5.17 Problems 171 6 Multivariate Analysis 175 6.1 Introduction 175 6.2 Breakdown and efficiency of multivariate estimates 180 6.2.1 Breakdown point 180 6.2.2 The multivariate exact fit property 181 6.2.3 Efficiency 181 6.3 M-estimates 182 6.3.1 Collinearity 184 6.3.2 Size and shape 185 6.3.3 Breakdown point 186 6.4 Estimates based on a robust scale 187 6.4.1 The minimum volume ellipsoid estimate 187 6.4.2 S-estimates 188 6.4.3 The minimum covariance determinant estimate 189 6.4.4 S-estimates for high dimension 190 6.4.5 One-step reweighting 193 6.5 The Stahel–Donoho estimate 193 6.6 Asymptotic bias 195 6.7 Numerical computation of multivariate estimates 197 6.7.1 Monotone M-estimates 197 6.7.2 Local solutions for S-estimates 197 6.7.3 Subsampling for estimates based on a robust scale 198 6.7.4 The MVE 199 6.7.5 Computation of S-estimates 199 CONTENTS xi 6.7.6 The MCD 200 6.7.7 The Stahel–Donoho estimate 200 6.8 Comparing estimates 200 6.9 Faster robust dispersion matrix estimates 204 6.9.1 Using pairwise robust covariances 204 6.9.2 Using kurtosis 208 6.10 Robust principal components 209 6.10.1 Robust PCA based on a robust scale 211 6.10.2 Spherical principal components 212 6.11 *Other estimates of location and dispersion 214 6.11.1 Projection estimates 214 6.11.2 Constrained M-estimates 215 6.11.3 Multivariate MM- and τ -estimates 216 6.11.4 Multivariate depth 216 6.12 Appendix: proofs and complements 216 6.12.1 Why affine equivariance? 216 6.12.2 Consistency of equivariant estimates 217 6.12.3 The estimating equations of the MLE 217 6.12.4 Asymptotic BP of monotone M-estimates 218 6.12.5 The estimating equations for S-estimates 220 6.12.6 Behavior of S-estimates for high p 221 6.12.7 Calculating the asymptotic covariance matrix of location M-estimates 222 6.12.8 The exact fit property 224 6.12.9 Elliptical distributions 224 6.12.10 Consistency of Gnanadesikan–Kettenring correlations 225 6.12.11 Spherical principal components 226 6.13 Problems 227 7 Generalized Linear Models 229 7.1 Logistic regression 229 7.2 Robust estimates for the logistic model 233 7.2.1 Weighted MLEs 233 7.2.2 Redescending M-estimates 234 7.3 Generalized linear models 239 7.3.1 Conditionally unbiased bounded influence estimates 242 7.3.2 Other estimates for GLMs 243 7.4 Problems 244 8 Time Series 247 8.1 Time series outliers and their impact 247 8.1.1 Simple examples of outliers’ influence 250 8.1.2 Probability models for time series outliers 252 8.1.3 Bias impact of AOs 256 xii CONTENTS 8.2 Classical estimates for AR models 257 8.2.1 The Durbin–Levinson algorithm 260 8.2.2 Asymptotic distribution of classical estimates 262 8.3 Classical estimates for ARMA models 264 8.4 M-estimates of ARMA models 266 8.4.1 M-estimates and their asymptotic distribution 266 8.4.2 The behavior of M-estimates in AR processes with AOs 267 8.4.3 The behavior of LS and M-estimates for ARMA processes with infinite innovations variance 268 8.5 Generalized M-estimates 270 8.6 Robust AR estimation using robust filters 271 8.6.1 Naive minimum robust scale AR estimates 272 8.6.2 The robust filter algorithm 272 8.6.3 Minimum robust scale estimates based on robust filtering 275 8.6.4 A robust Durbin–Levinson algorithm 275 8.6.5 Choice of scale for the robust Durbin–Levinson procedure 276 8.6.6 Robust identification of AR order 277 8.7 Robust model identification 278 8.7.1 Robust autocorrelation estimates 278 8.7.2 Robust partial autocorrelation estimates 284 8.8 Robust ARMA model estimation using robust filters 287 8.8.1 τ -estimates of ARMA models 287 8.8.2 Robust filters for ARMA models 288 8.8.3 Robustly filtered τ -estimates 290 8.9 ARIMA and SARIMA models 291 8.10 Detecting time series outliers and level shifts 294 8.10.1 Classical detection of time series outliers and level shifts 295 8.10.2 Robust detection of outliers and level shifts for ARIMA models 297 8.10.3 REGARIMA models: estimation and outlier detection 300 8.11 Robustness measures for time series 301 8.11.1 Influence function 301 8.11.2 Maximum bias 303 8.11.3 Breakdown point 304 8.11.4 Maximum bias curves for the AR(1) model 305 8.12 Other approaches for ARMA models 306 8.12.1 Estimates based on robust autocovariances 306 8.12.2 Estimates based on memory-m prediction residuals 308 8.13 High-efficiency robust location estimates 308 8.14 Robust spectral density estimation 309 8.14.1 Definition of the spectral density 309 8.14.2 AR spectral density 310 8.14.3 Classic spectral density estimation methods 311 8.14.4 Prewhitening 312 CONTENTS xiii 8.14.5 Influence of outliers on spectral density estimates 312 8.14.6 Robust spectral density estimation 314 8.14.7 Robust time-average spectral density estimate 316 8.15 Appendix A: heuristic derivation of the asymptotic distribution of M-estimates for ARMA models 317 8.16 Appendix B: robust filter covariance recursions 320 8.17 Appendix C: ARMA model state-space representation 322 8.18 Problems 323 9 Numerical Algorithms 325 9.1 Regression M-estimates 325 9.2 Regression S-estimates 328 9.3 The LTS-estimate 328 9.4 Scale M-estimates 328 9.4.1 Convergence of the fixed point algorithm 328 9.4.2 Algorithms for the nonconcave case 330 9.5 Multivariate M-estimates 330 9.6 Multivariate S-estimates 331 9.6.1 S-estimates with monotone weights 331 9.6.2 The MCD 332 9.6.3 S-estimates with nonmonotone weights 333 9.6.4 *Proof of (9.25) 334 10 Asymptotic Theory of M-estimates 335 10.1 Existence and uniqueness of solutions 336 10.2 Consistency 337 10.3 Asymptotic normality 339 10.4 Convergence of the SC to the IF 342 10.5 M-estimates of several parameters 343 10.6 Location M-estimates with preliminary scale 346 10.7 Trimmed means 348 10.8 Optimality of the MLE 348 10.9 Regression M-estimates 350 10.9.1 Existence and uniqueness 350 10.9.2 Asymptotic normality: fixed X 351 10.9.3 Asymptotic normality: random X 355 10.10 Nonexistence of moments of the sample median 355 10.11 Problems 356 11 Robust Methods in S-Plus 357 11.1 Location M-estimates: function Mestimate 357 11.2 Robust regression 358 11.2.1 A general function for robust regression: lmRob 358 11.2.2 Categorical variables: functions as.factor and contrasts 361 xiv CONTENTS 11.2.3 Testing linear assumptions: function rob.linear.test 363 11.2.4 Stepwise variable selection: function step 364 11.3 Robust dispersion matrices 365 11.3.1 A general function for computing robust location–dispersion estimates: covRob 365 11.3.2 The SR-α estimate: function cov.SRocke 366 11.3.3 The bisquare S-estimate: function cov.Sbic 366 11.4 Principal components 366 11.4.1 Spherical principal components: function prin.comp.rob 367 11.4.2 Principal components based on a robust dispersion matrix: function princomp.cov 367 11.5 Generalized linear models 368 11.5.1 M-estimate for logistic models: function BYlogreg 368 11.5.2 Weighted M-estimate: function WBYlogreg 369 11.5.3 A general function for generalized linear models: glmRob 370 11.6 Time series 371 11.6.1 GM-estimates for AR models: function ar.gm 371 11.6.2 Fτ -estimates and outlier detection for ARIMA and REGARIMA models: function arima.rob 372 11.7 Public-domain software for robust methods 374 12 Description of Data Sets 377 Bibliography 383 Index

这本书出自 Richard A. Johnson (Author), Dean W. Wichern (Author) 两位著名的教授，是这个领域比较著名的数，在工业工程，经济管理，工程科技等涉及多变量的领域应用广泛。仅仅用作学习目的，阅后请删除。

MLBQTEST(X,LAGS) 执行多变量 Portmanteau 检验。 h = mlbqtest(X,LAGS) 返回 LAGS 的逻辑值 (h)，拒绝决定来自对多元系列 X 中的联合互相关进行多元 Portmanteau 检验。 h = mlbqtest(X,LAGS,ALPHA) 指定显着性水平（默认值 = 0.05）。 [h,pValue] = mlbqtest(~) 返回假设检验的拒绝决定和 p 值。 [h,pValue,stat,cValue] = mlbqtest(~) 还返回假设检验的检验统计量 (stat) 和临界值 (cValue)。 输入参数 X：具有 k 个资产和 T 次的多元时间序列 (T xk)。 检验原假设 H0：所有相关系数为零，即。 rho_1=rho_2=...rho_m=0，其中 m 滞后备择假设H1：有一些系数不为零。

Multivariate Analysis,Mardia 1979的著作，网上很难找到的pdf。

多元统计分析经典教材， 介绍高维正态分布，SVD矩阵分解，PCA降维方法等常用的统计方法。

narx的matlab代码多元多步风速预测 该呼吸包含使用多变量输入数据进行的单步和多步风速预测的实现代码。 这个想法是使用外部参数（例如温度，湿度，压力等）来预测不同层位的风速（向前迈进），而不涉及风速本身的历史数据。 使用了两种主要方法，包括各种深度学习迁移学习方法和常规神经网络模型。 数据库 风力数据库是从M2塔的获得的。 每两秒钟获取一次M2塔数据，并在不同高度（2至80 m）下测量的一分钟内取平均值。 但是，出于预测目的，我们将数据下采样到10分钟（平均）。 可以访问已处理的数据。 数据进行了分区，以将一年（2017）用于培训和验证（80％和20％），并将另一年（2018）用于测试目的。 转移学习 预训练的深度学习模型用于该实验。 该代码在中实现。 神经网络 文献中提出的几种神经网络方法已在以下方面实现：前馈神经网络（FFNN），时延神经网络（TDNN），非线性自回归外生模型（NARX）。

MVG 是一种多元高斯（正态）随机数生成器。 用户可以通过指定均值向量和对称正定协方差矩阵，从任何维度的多元正态分布生成向量。 基于协方差矩阵的 Cholesky 分解的线性变换应用于分布 N(0,I) 的一组实现。 通过对这些样本应用线性变换，输出是一个矩阵，其列是从分布 N(mu,Sigma) 中抽取的样本，其中 mu 是指定的均值向量，Sigma 是 SPD 协方差矩阵。 输入 help mvg 以了解更多信息。

an introduction to multivariate statistical