• Mastering Statistics with R
  • Welcome
    • Prerequisites
    • Who am I ?
    • Acknowledgement
    • Progress of this book
  • Part I: Foundations
  • 1 Probability Concept
    • 1.1 Introduction to Probability
      • 1.1.1 What is probability ?
      • 1.1.2 Basic Mathematic
      • 1.1.3 Set Theory
      • 1.1.4 History of probability
      • 1.1.5 Definitions of Probability
      • 1.1.6 Conditional Probability and Independence
      • 1.1.7 Bayes’ Theorem
    • 1.2 Random variables
      • 1.2.1 Random variables and probability functions
      • 1.2.2 Expected values and Variance
      • 1.2.3 Transformation of random variables
      • 1.2.4 Families of distributions
    • 1.3 Multivariate random variables
      • 1.3.1 Joint distributions
      • 1.3.2 Change of variables
      • 1.3.3 Families of multivariate distributions
    • 1.4 System of Moments
    • 1.5 Limit Theorem
      • 1.5.1 Some inequality
      • 1.5.2 Law of large numbers
  • 2 Basic Statistic
    • 2.1 Descriptive Statistics
      • 2.1.1 Frequency distribution
      • 2.1.2 Measures of statistical characteristics
      • 2.1.3 Exploratory data analysis (EDA)
    • 2.2 Sampling
      • 2.2.1 Random sampling methods (probability sampling techniques)
      • 2.2.2 Nonrandom sampling methods (non-probability sampling techniques)
      • 2.2.3 Other sampling methods
    • 2.3 Estimation
      • 2.3.1 Point Estimation
      • 2.3.2 Interval Estimation
    • 2.4 Testing Hypotheses
      • 2.4.1 Null hypothesis vs. alternative hypothesis
      • 2.4.2 The Neyman-Pearson Lemma
    • 2.5 Some statistical test
      • 2.5.1 Parametric statistical test
      • 2.5.2 Non-parametric statistical test
    • 2.6 Analysis of Variance (ANOVA)
      • 2.6.1 Levene’s test
      • 2.6.2 Bartlett’s test
      • 2.6.3 One-way ANOVA
      • 2.6.4 Two-way ANOVA
      • 2.6.5 Welch’s ANOVA
      • 2.6.6 Kruskal–Wallis test
      • 2.6.7 Friedman test
      • 2.6.8 Normality Test
    • 2.7 Correlation Analysis and Linear Regression
  • 3 Mathematical Statistics
    • 3.1 Limiting Distributions
      • 3.1.1 Converge in probability
      • 3.1.2 Converge in distribution
  • Part II: Methodology - Beginner
  • 4 Probability Models
    • 4.1 Stochastic Process
      • 4.1.1 Random Walk
      • 4.1.2 Poisson Process
      • 4.1.3 Markov Process
      • 4.1.4 Wiener Process
      • 4.1.5 Lévy Process
    • 4.2 Markov Chain
      • 4.2.1 Semi-Markov Chain
      • 4.2.2 Hidden Markov Models
      • 4.2.3 Mover-Stayer Models
    • 4.3 Ergodic Theory
    • 4.4 Degradation data
    • 4.5 Stochastic Calculus
      • 4.5.1 Ito’s Lemma
  • 5 Regression Analysis
    • 5.1 Ordinary Least Squares (OLS)
      • 5.1.1 Variable Selection
      • 5.1.2 Model Selection
    • 5.2 Fixed Effect and Random Effect
    • 5.3 Analysis of Covariance (ANCOVA)
    • 5.4 Logistic Regression
    • 5.5 Fractional Model
  • 6 Categorical Data Analysis
  • 7 Multivariate Analysis
    • 7.1 Multivariate distributions
    • 7.2 General Linear Model
    • 7.3 Multivariate Analysis of Variance (MANOVA)
    • 7.4 Multivariate Analysis of Covariance (MANCOVA)
    • 7.5 Structural Equation Modeling (SEM)
    • 7.6 Dimension Reduction Method
      • 7.6.1 t-SNE
      • 7.6.2 DBSCAN
      • 7.6.3 Locally Linear Embedding
      • 7.6.4 Laplacian Eigenmaps
      • 7.6.5 ISOMAP
      • 7.6.6 Uniform Manifold Approximation and Projection (UMAP)
    • 7.7 Clustering Method
      • 7.7.1 K-means, K-medoids
      • 7.7.2 KNN
      • 7.7.3 Principal Component Analysis (PCA)
      • 7.7.4 Principal Co-ordinates Analysis (PCoA)
      • 7.7.5 Multidimensional Scaling (MDS)
      • 7.7.6 Self-organizing map (SOM)
      • 7.7.7 Spectral clustering
      • 7.7.8 Quantum clustering
      • 7.7.9 Partial Least Squares Discriminant Analysis (PLS-DA)
      • 7.7.10 Unweighted Paired-Group Method Using Arithmetic Means (UPGMA)
    • 7.8 Factor Analysis
      • 7.8.1 Kaiser–Meyer–Olkin test
      • 7.8.2 Questionnaire
    • 7.9 Canonical-correlation Analysis (CCA)
    • 7.10 Analysis of Similarities (ANOSIM)
  • 8 Time Series Analysis
    • 8.1 Time Series Decomposition
    • 8.2 ACF and PACF
    • 8.3 White Noise
    • 8.4 Autoregressive (AR)
    • 8.5 Moving Average (MA)
    • 8.6 Kalman Filter and Savitzky–Golay filter
    • 8.7 ARMA, ARIMA, SARIMA, SARFIMA
    • 8.8 Granger causality
    • 8.9 VAR
    • 8.10 GARCH
    • 8.11 Factor Model
    • 8.12 Some advanced topics
      • 8.12.1 Lag regression
      • 8.12.2 Mixed-frequency data
  • Part III: Methodology - Advanced
  • 9 Generalized Linear Models
    • 9.1 Weighted Least Square (WLS) and Generalized Least Square (GLS)
      • 9.1.1 Rootogram
    • 9.2 Complex Linear Model
    • 9.3 Generalized Estimating Equation (GEE)
    • 9.4 Hierarchical Linear Model
      • 9.4.1 Instrumental variable
    • 9.5 Multilevel Model
  • 10 Spatial Statistics
    • 10.1 Point-referenced Data
      • 10.1.1 Gaussian Process
      • 10.1.2 Exploratory data analysis
      • 10.1.3 Models for spatial dependence
      • 10.1.4 Kriging (Spatial prediction)
    • 10.2 Areal/Lattice Data
      • 10.2.1 Spatial autocorrelation
      • 10.2.2 Conditionally auto-regressive (CAR) and Simultaneously auto-regressive (SAR) models
    • 10.3 Point Pattern Data
      • 10.3.1 Poisson processes
      • 10.3.2 Cox processes
      • 10.3.3 K-functions
    • 10.4 Other Topics
      • 10.4.1 Spatio-temporal models
      • 10.4.2 Frequency domain methods
      • 10.4.3 Deep Kriging
  • 11 Functional Data Analysis
  • 12 Bayesian Analysis
    • 12.1 Laplace Approximation and BIC
  • 13 High Dimensional Data
  • Part IV: Methodology - Others
  • 14 Non-parametric model
    • 14.1 Quantile Regression
    • 14.2 LOESS
    • 14.3 Curve estimation
      • 14.3.1 Kernel
  • 15 Extreme value theory
  • 16 Directional Statistics
    • 16.1 Circular Regression
  • 17 Topological Data Analysis
  • Part V: Application - Industrial Statistics
  • 18 Quality Control
    • 18.1 History
    • 18.2 7 tools
    • 18.3 ARL
    • 18.4 \(R\) chart
    • 18.5 \(s\) chart
    • 18.6 \(\bar{X}\) chart
    • 18.7 \(p\) chart
    • 18.8 CUSUM
    • 18.9 EWMA
    • 18.10 Sequential probability ratio test
  • 19 Reliability Analysis
  • 20 Design of Experiments
    • 20.1 Latin hypercube
    • 20.2 Sequential design
    • 20.3 Space-filling design
    • 20.4 Active learning (Optimal experimental design)
    • 20.5 Online machine learning
  • Part VI: Application - Biostatistic
  • 21 Biostatistical Data Analysis
    • 21.1 p-value correction
      • 21.1.1 Bofferoni
      • 21.1.2 Tukey’s HSD
      • 21.1.3 Fisher
      • 21.1.4 False Discovery Rate (FDR)
      • 21.1.5 Q-value
      • 21.1.6 E-value
    • 21.2 Trend Tests
      • 21.2.1 Cochran-Armitage test
      • 21.2.2 Jonckheere’s trend test
    • 21.3 Propensity score
    • 21.4 PLINK
    • 21.5 Polygenic Risk Score
    • 21.6 RNA-seq Analysis
    • 21.7 Metabolomics Analysis
      • 21.7.1 SMART
      • 21.7.2 pareto normalization
    • 21.8 Permutational multivariate analysis of variance (PERMANOVA)
    • 21.9 PERMDISP
  • 22 Survial Analysis
    • 22.1 Unobserved data
    • 22.2 Survival Function and Hazard Function
    • 22.3 Kaplan–Meier Estimator
    • 22.4 Log-rank Test
    • 22.5 Proportional Hazards Model
    • 22.6 Accelerated Failure Time (AFT) Model
    • 22.7 Nelson–Aalen Estimator
    • 22.8 Turnbull-Frydman Estimator
    • 22.9 Restricted Median Survival Time (RMST)
    • 22.10 Firth’s penalized logistic regression
    • 22.11 Competing Risks
  • 23 Causal Inference
    • 23.1 DAG
  • 24 Statistical Designs and Analyses in Clinical Trials
    • 24.1 Phase I
    • 24.2 Phase II
    • 24.3 Phase III
    • 24.4 \(\alpha\) spending function
  • Part VII: Application - Others
  • 25 Financial Statistics
  • 26 Social Statistics
    • 26.1 Human Behaviour
    • 26.2 Sport
  • 27 Psychometrics
    • 27.1 Item Response Theory
  • Part VIII: Computational Statistics
  • 28 Statistical Learning
    • 28.1 Root finding
      • 28.1.1 Newton’s method (Newton–Raphson algorithm)
      • 28.1.2 Gauss–Newton algorithm
      • 28.1.3 Gradient Descent
      • 28.1.4 Conjugate gradient method
      • 28.1.5 Nelder–Mead method
      • 28.1.6 Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm
    • 28.2 Information Criteria
      • 28.2.1 AIC
      • 28.2.2 BIC
    • 28.3 Desicion Tree and Random Forest
    • 28.4 Bagging
    • 28.5 Boosting
      • 28.5.1 Gradient Boost Desicion Tree (GBDT)
      • 28.5.2 XGBoost
      • 28.5.3 LightGBM
      • 28.5.4 CATBoost
      • 28.5.5 RUSBoost
  • 29 Statistical Computing
    • 29.1 Generate random variables
      • 29.1.1 Inverse transform method
      • 29.1.2 Accept-Rejection method
      • 29.1.3 Importance Sampling
    • 29.2 Variance reduction
    • 29.3 Gibbs sampling
    • 29.4 Metropolis-Hastings
    • 29.5 Monte-Carlo and Markov chain (MCMC)
    • 29.6 EM algorithm
    • 29.7 Back-fitting algorithm
    • 29.8 Particle Swarm Optimization (PSO)
  • 30 Advanced Machine Learning
    • 30.1 Double Machine Learning
    • 30.2 Adversarial machine learning (AML)
    • 30.3 Reinforcement Learning
    • 30.4 Curriculum learning
    • 30.5 Rule-based machine learning
    • 30.6 Online machine learning
    • 30.7 Quantum Machine Learning
    • 30.8 Computational Learning Theory
      • 30.8.1 Probably Approximately Correct (PAC)
  • 31 Deep Learning
    • 31.1 Basic concept
    • 31.2 DNN
    • 31.3 CNN
    • 31.4 RNN
      • 31.4.1 Long Short-Term Memory (LSTM)
      • 31.4.2 Gated Recurrent Unit (GRU)
    • 31.5 Generative adversarial networks (GAN)
    • 31.6 Transformer Networks
    • 31.7 Autoencoders & Variational Autoencoders (VAEs)
    • 31.8 Graph Neural Networks (GNNs)
    • 31.9 Physics-informed neural networks (PINNs)
    • 31.10 Deep Q-Networks (DQNs)
    • 31.11 Quantum neural network (QNN)
    • 31.12 Some famous models
      • 31.12.1 LeNet、AlexNet、VGG、NiN
      • 31.12.2 GoogLeNet
      • 31.12.3 ResNet
      • 31.12.4 DenseNet
      • 31.12.5 YOLO
    • 31.13 Modern NN models
      • 31.13.1 Liquid Neural Network (LNN)
      • 31.13.2 Kolmogorov-Arnold Networks (KAN)
  • Part IX: Deal with Computer Science
  • 32 Data Structure and Algorithm
    • 32.1 Data Structure
      • 32.1.1 Linked list
      • 32.1.2 Satck
      • 32.1.3 Queue
      • 32.1.4 Tree
    • 32.2 Algorithm
      • 32.2.1 Graph and tree traversal algorithms
  • 33 Information Theory
    • 33.1 Entropy
    • 33.2 Data compression
  • 34 Big Data Analytics Techniques and Applications
    • 34.1 Visualization
    • 34.2 Hadoop
    • 34.3 Spark
  • 35 Multimodal Data Analysis
    • 35.1 Network Analysis
    • 35.2 Image and Video Analysis
    • 35.3 Audio Analysis
    • 35.4 Text Mining
  • Part X: Data Communication
  • 36 Data Processing
    • 36.1 Data Collection
    • 36.2 Data Preprocessing
      • 36.2.1 Data Cleaning
      • 36.2.2 Handling Missing Data
      • 36.2.3 Normalization & Standardization
      • 36.2.4 Feature Engineering
  • 37 Data Visualization
    • 37.1 Visual Analytics
    • 37.2 Radar chart
    • 37.3 Parallel coordinates
    • 37.4 Andrews plot
    • 37.5 Fish plot
    • 37.6 Circle Packing Chart
    • 37.7 Chord diagram (information visualization)
    • 37.8 Climate spiral and Warming stripes
    • 37.9 Symbolic data analysis (SDA)
  • 38 Data Mining
    • 38.1 Association rule learning
    • 38.2 Anomaly detection
  • 39 Statistical Consulting
  • Part XI: Data Integration
  • 40 Data Management
    • 40.1 Database
    • 40.2 SQL
    • 40.3 Data Compression
    • 40.4 Data Integration
  • 41 DataOps
  • 42 Meta Analysis
    • 42.1 Federated Learning
  • Part XII: Statistic Theory
  • 43 Statistical Inference
    • 43.1 Frequentist inference
    • 43.2 Bayesian inference
  • 44 Decision Theory
    • 44.1 Regret
  • 45 Probability Theory
    • 45.1 Basics from Measure Theory
    • 45.2 Limit of the sets
    • 45.3 Probability Inequalities
    • 45.4 Stochastic ordering
    • 45.5 Malliavin Calculus
    • 45.6 Regular conditional probability
      • 45.6.1 Markov kernel
    • 45.7 Martingale
      • 45.7.1 Reverse martingale
  • 46 Algebraic Statistics
  • 47 Free Probability Theory
  • Part VIII: Miscellaneous
  • 48 Statistical Education
  • 49 Ethics and Philosophy
    • 49.1 Differential Privacy
  • Appendix
  • A Matrix calculus
  • B Advanced programming in R
    • B.1 Technique for Basic operator
    • B.2 Special operator
      • B.2.1 Inner function
      • B.2.2 Super assignment <<-
    • B.3 Pipe operator
      • B.3.1 User define pipe operator
    • B.4 Non-standard Evaluation (NSE)
      • B.4.1 Tidy evaluation
    • B.5 Functional programming
      • B.5.1 Helper function
    • B.6 Progress bar
    • B.7 Parallel computing
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Mastering Statistics with R

30.2 Adversarial machine learning (AML)

https://en.wikipedia.org/wiki/Adversarial_machine_learning https://medium.com/trustableai/%E9%87%9D%E5%B0%8D%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92%E7%9A%84%E6%83%A1%E6%84%8F%E8%B3%87%E6%96%99%E6%94%BB%E6%93%8A-%E4%B8%80-e94987742767 https://medium.com/trustableai/%E9%81%8B%E7%94%A8%E5%B0%8D%E6%8A%97%E4%BE%8B%E6%94%BB%E6%93%8A%E6%B7%B1%E5%BA%A6%E5%AD%B8%E7%BF%92%E6%A8%A1%E5%9E%8B-%E4%BA%8C-30f80a3e7429 https://www.datacamp.com/blog/adversarial-machine-learning