svd算法matlab代码高维设计差分私有估计器 高维差分私有鲁棒均值估计器的MATLAB实现。 先决条件 用于在较大矩阵上进行特征值计算的MATLAB软件包 算法实现 dpCode目录包含各种差分私有均值估计算法的实现代码。 dpFilterGaussianMean.m ：本文提出的新型差分私有算法的主要实现 filterGaussianMean.m ：通过过滤实现稳健的均值估计，取自，并在ICML 2017的论文中进行了描述 dpWinsorizedMean.m ：的差分私有dpWinsorizedMean.m Mean（算法1）的实现 privateQuantile.m ：实用程序函数，包含来自的privateQuantile算法（算法2）的实现 laplaceSample.m ：实用函数，通过逆CDF采样从Laplace分布中提取噪声样本 我们的算法仅需要前两个文件，而后两个文件则来自的DP Winsorized均值参考算法。 最后一个文件（ laplaceSample.m ）是两种算法中使用的Laplace机制的实用程序实现。 重现性 compareDPMeanEstimat

鲁棒自适应控制的英文教材 值得一看! Petros A. Ioannou 的书

2.1 基基基本本本原原原理理理 因为R是一种编程语言，一些对编程不太熟悉的人可能会望而却步。这种 障碍其实是完全没有必要，首先，R是一种解释型语言，而不是编译语言，也 就意味着输入的命令能够直接被执行，而不需要像一些语言要首先构成一个 完整的程序形式(如C，Fortan, Pascal, . . . )。 第二，R的语法非常之简单和直观。例如，线性回归的命令lm(y ~ x) 表 示“以x为自变量，y为反应量来拟合一个线性模型”。合法的R函数总是带有 圆括号的形式，即使括号内没有内容(如,ls())。如果直接输入函数名而不输 入圆括号，R则会自动显示该函数的一些具体内容。在本手册中除在部分文字 已作出清楚的说明外，所有的函数后都接有圆括号以区别于对象(object)。 当R运行时，所有变量，数据，函数及结果都以对象(objects)的形式存 在计算机的活动内存中，并冠有相应的名字代号。我们可以通过用一些运算 符(如算术，逻辑，比较等)和一些函数(其本身也是对象)来对这些对象进行操 作。运算操作非常简单，其细节将留在下章讨论(p. 26). 关于R中的函数可用 下面的图例来形象的描述： arguments −→ options −→ function ↑ default arguments =⇒result 上图中的参量(argument)可能是一些对象(如数据，方程，算式. . . )。有 些参量在函数里被预设为缺省值，用户则可按需对其作个别的修改。所以运 行一个R函数可能不需要设定任何参量，原因是所有的参量都可以被默认为缺 3

稳健的电力系统状态估计器对于监控应用至关重要。 根据我们的经验，我们发现使用投影统计的鲁棒广义最大似然（GM）估计是文献中最好的方法之一。 它对多个交互和一致的不良数据、不良杠杆点、不良零注入以及某些类型的网络攻击具有鲁棒性。 此外，其计算效率高，适合在线应用。 除了 GM 估计器的良好击穿点外，它在高斯或其他厚尾非高斯测量噪声下具有很高的统计效率。 使用 SCADA 测量的 GM 估计器的原始版本是由 Mili 和他的同事在 1996 年提出的 [1]。 通过在 [R2] 中使用 Givens 旋转增强了其数值稳定性。 在 [R3] 中，GM 估计器被扩展为同时估计变压器抽头位置和系统状态。 错误的零注射也得到了解决。 在 [R4] 中，提出了 GM 估计器来处理动态状态估计中的创新和观察异常值以及测量损失。 在这里，我们想与所有研究人员分享 GM 估计器的 Matlab 代码。 我们还

使用对抗性训练增强深度学习以稳健预测癫痫发作 该存储库包含 Hussein A.、Djandji M. 等人在 ACM Transactions on Computing for Healthcare 发表的期刊论文“Augmenting DL with Adversarial Training for Robust Prediction of Epilepsy Seizures”中使用的代码。 该论文可以在这里找到： : 。 要求 h5py (2.9.0) 希克尔 (3.4.5) matplotlib (3.1.1) 内 (0.11.0) 熊猫 (0.25.1) scikit-learn (0.21.3) scipy (1.1.0) 张量流-GPU (1.14.0) 主文件夹说明 CHBMIT 和 FB：原始数据集文件夹。 CHBMIT_cache 和 FB_cach

这是一篇关于不确定时滞系统的巨著，里面介绍了时滞系统滤波器设计的方法，也给出了好多处理不确定性的方法，是值得一读的专著

Calafiore G,CampiMC.Uncertainconvexprograms:randomizedsolutionsand confidence levels.MathematicalProgramming2005;102(1):25–46.

matlab超声成像代码通用深度波束成形器适用于稳健的超声成像 “用于鲁棒超声成像的通用深波束成形器”的计算机代码和数据集 纸 Shujaat Khan、Jaeyoung Huh 和 Jong Chul Ye。 “用于可变速率超声成像的通用深波束成形器。” . 执行 MatConvNet (matconvnet-1.0-beta24) 请运行 matconvnet-1.0-beta24/matlab/vl_compilenn.m 文件来编译 matconvnet。 有关于“”的说明 请运行安装设置 (install.m) 并运行一些训练示例。 训练网络 上传了“通用深波束成形器 CNN”的训练网络。 使用第 100 个或第 200 个纪元权重进行验证。 （论文中的结果是使用第 200 个时期的权重生成的） 测试数据 样本测试数据放置在“data”文件夹中。 数据维度如下——Test_data = 3x96x64x2048（输入平面x扫描线x通道x深度） 使用建议的算法执行测试 -> 运行“DeepBF_Test.m”

python库，解压后可用。 资源全名：robust_laplacian-0.2.1-cp39-cp39-win_amd64.whl

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