Tabla de Contenidos: “…Front Cover; Contents; Preface; Chapter 1: Introduction; Chapter 2: The Lasso for Linear Models; Chapter 3: Generalized Linear Models; Chapter 4: Generalizations of the Lasso Penalty; Chapter 5: Optimization Methods; Chapter 6: Statistical Inference; Chapter 7: Matrix Decompositions, Approximations, and Completion; Chapter 8: Sparse Multivariate Methods; Chapter 9: Graphs and Model Selection; Chapter 10: Signal Approximation and Compressed Sensing; Chapter 11: Theoretical Results for the Lasso; Bibliography; Back Cover…”

“…Uncertainty quantification (UQ) is a mainstream research topic in applied mathematics and statistics. To identify UQ problems, diverse modern techniques for large and complex data analyses have been developed in applied mathematics, computer science, and statistics. …”

Tabla de Contenidos: “…Front Cover; Contents; Foreword; Preface; About the Editor; Contributors; Chapter 1: Canadians Studying Abroad and the Development of Statistics in Canada; Chapter 2: Some of Statistics Canada's Contributions to Survey Methodology; Chapter 3: Watching Children Grow Taught Us All We Know; Chapter 4: Modeling Dependence beyond Correlation; Chapter 5: Lasso and Sparsity in Statistics; Chapter 6: Optimizing and Adapting the Metropolis Algorithm; Chapter 7: Design of Computer Experiments for Optimization, Estimation of Function Contours, and Related Objectives…”

Tabla de Contenidos: “…5.3.1 Properties of M-estimators with a bounded 𝝆-function 120 -- 5.4 Estimators based on a robust residual scale 124 -- 5.4.1 S-estimators 124 -- 5.4.2 L-estimators of scale and the LTS estimator 126 -- 5.4.3 𝜏−estimators 127 -- 5.5 MM-estimators 128 -- 5.6 Robust inference and variable selection for M-estimators 133 -- 5.6.1 Bootstrap robust confidence intervals and tests 134 -- 5.6.2 Variable selection 135 -- 5.7 Algorithms 138 -- 5.7.1 Finding local minima 140 -- 5.7.2 Starting values: the subsampling algorithm 141 -- 5.7.3 A strategy for faster subsampling-based algorithms 143 -- 5.7.4 Starting values: the Peña-Yohai estimator 144 -- 5.7.5 Starting values with numeric and categorical predictors 146 -- 5.7.6 Comparing initial estimators 149 -- 5.8 Balancing asymptotic bias and efficiency 150 -- 5.8.1 “Optimal” redescending M-estimators 153 -- 5.9 Improving the efficiency of robust regression estimators 155 -- 5.9.1 Improving efficiency with one-step reweighting 155 -- 5.9.2 A fully asymptotically efficient one-step procedure 156 -- 5.9.3 Improving finite-sample efficiency and robustness 158 -- 5.9.4 Choosing a regression estimator 164 -- 5.10 Robust regularized regression 164 -- 5.10.1 Ridge regression 165 -- 5.10.2 Lasso regression 168 -- 5.10.3 Other regularized estimators 171 -- 5.11 *Other estimators 172 -- 5.11.1 Generalized M-estimators 172 -- 5.11.2 Projection estimators 174 -- 5.11.3 Constrained M-estimators 175 -- 5.11.4 Maximum depth estimators 175 -- 5.12 Other topics 176 -- 5.12.1 The exact fit property 176 -- 5.12.2 Heteroskedastic errors 177 -- 5.12.3 A robust multiple correlation coefficient 180 -- 5.13 *Appendix: proofs and complements 182 -- 5.13.1 The BP of monotone M-estimators with random X 182 -- 5.13.2 Heavy-tailed x 183 -- 5.13.3 Proof of the exact fit property 183 -- 5.13.4 The BP of S-estimators 184 -- 5.13.5 Asymptotic bias of M-estimators 186 -- 5.13.6 Hampel optimality for GM-estimators 187 -- 5.13.7 Justification of RFPE∗ 188.…”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Journey from Statistics to Machine Learning -- Statistical terminology for model building and validation -- Machine learning -- Major differences between statistical modeling and machine learning -- Steps in machine learning model development and deployment -- Statistical fundamentals and terminology for model building and validation -- Bias versus variance trade-off -- Train and test data -- Machine learning terminology for model building and validation -- Linear regression versus gradient descent -- Machine learning losses -- When to stop tuning machine learning models -- Train, validation, and test data -- Cross-validation -- Grid search -- Machine learning model overview -- Summary -- Chapter 2: Parallelism of Statistics and Machine Learning -- Comparison between regression and machine learning models -- Compensating factors in machine learning models -- Assumptions of linear regression -- Steps applied in linear regression modeling -- Example of simple linear regression from first principles -- Example of simple linear regression using the wine quality data -- Example of multilinear regression - step-by-step methodology of model building -- Backward and forward selection -- Machine learning models - ridge and lasso regression -- Example of ridge regression machine learning -- Example of lasso regression machine learning model -- Regularization parameters in linear regression and ridge/lasso regression -- Summary -- Chapter 3: Logistic Regression Versus Random Forest -- Maximum likelihood estimation -- Logistic regression - introduction and advantages -- Terminology involved in logistic regression -- Applying steps in logistic regression modeling…”

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Tabla de Contenidos: “…Step 4: Accuracy and error -- Summary -- Chapter 7: Regularization for Database Improvement -- Statistical regularization -- Various statistical regularization methods -- Ridge -- Lasso -- Least angles -- Opportunities for regularization -- Collinearity -- Sparse solutions -- High-dimensional data -- Classification -- Using data to understand statistical regularization -- Improving data or a data model -- Simplification -- Relevance -- Speed -- Transformation -- Variation of coefficients -- Casual inference -- Back to regularization -- Reliability -- Using R for statistical regularization -- Parameter Setup -- Summary -- Chapter 8: Database Development and Assessment -- Assessment and statistical assessment -- Objectives -- Baselines -- Planning for assessment -- Evaluation -- Development versus assessment -- Planning -- Data assessment and data quality assurance -- Categorizing quality -- Relevance -- Cross-validation -- Preparing data -- R and statistical assessment -- Questions to ask -- Learning curves -- Example of a learning curve -- Summary -- Chapter 9: Databases and Neural Networks -- Ask any data scientist -- Defining neural network -- Nodes -- Layers -- Training -- Solution -- Understanding the concepts -- Neural network models and database models -- No single or main node -- Not serial -- No memory address to store results -- R-based neural networks -- References -- Data prep and preprocessing -- Data splitting -- Model parameters -- Cross-validation -- R packages for ANN development -- ANN -- ANN2 -- NNET -- Black boxes -- A use case -- Popular use cases -- Character recognition -- Image compression -- Stock market prediction -- Fraud detection -- Neuroscience -- Summary -- Chapter 10: Boosting your Database -- Definition and purpose -- Bias -- Categorizing bias -- Causes of bias -- Bias data collection -- Bias sample selection…”

“…Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this highly accessible text: Describes the challenges related to the analysis of high-dimensional data Covers cutting-edge statistical methods including model selection, sparsity and the lasso, aggregation, and learning theory Provides detailed exercises at the end of every chapter with collaborative solutions on a wikisite Illustrates concepts with simple but clear practical examples Introduction to High-Dimensional Statistics is suitable for graduate students and researchers interested in discovering modern statistics for massive data. …”

“…Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this new edition:Offers revised chapters from the previous edition, with the inclusion of many additional materials on some important topics, including compress sensing, estimation with convex constraints, the slope estimator, simultaneously low-rank and row-sparse linear regression, or aggregation of a continuous set of estimators.Introduces three new chapters on iterative algorithms, clustering, and minimax lower bounds.Provides enhanced appendices, minimax lower-bounds mainly with the addition of the Davis-Kahan perturbation bound and of two simple versions of the Hanson-Wright concentration inequality.Covers cutting-edge statistical methods including model selection, sparsity and the Lasso, iterative hard thresholding, aggregation, support vector machines, and learning theory.Provides detailed exercises at the end of every chapter with collaborative solutions on a wiki site.Illustrates concepts with simple but clear practical examples…”

Tabla de Contenidos: “…Generating a bootstrap distribution for the median -- Constructing the bootstrapped confidence interval -- Re-centering a bootstrap distribution -- Introducing the central limit theorem used in t-distribution -- Constructing the confidence interval for the population mean using the t-distribution -- Performing hypothesis testing for two means -- Introducing ANOVA -- Summary -- Chapter 12: Linear Regression in R -- Introducing linear regression -- Understanding simple linear regression -- Introducing multiple linear regression -- Seeking a higher coefficient of determination -- More on adjusted ​​R​​ 2​​ -- Developing an MLR model -- Introducing Simpson's Paradox -- Working with categorical variables -- Introducing the interaction term -- Handling nonlinear terms -- More on the logarithmic transformation -- Working with the closed-form solution -- Dealing with multicollinearity -- Dealing with heteroskedasticity -- Introducing penalized linear regression -- Working with ridge regression -- Working with lasso regression -- Summary -- Chapter 13: Logistic Regression in R -- Technical requirements -- Introducing logistic regression -- Understanding the sigmoid function -- Grokking the logistic regression model -- Comparing logistic regression with linear regression -- Making predictions using the logistic regression model -- More on log odds and odds ratio -- Introducing the cross-entropy loss -- Evaluating a logistic regression model -- Dealing with an imbalanced dataset -- Penalized logistic regression -- Extending to multi-class classification -- Summary -- Chapter 14: Bayesian Statistics -- Technical requirements -- Introducing Bayesian statistics -- A first look into the Bayesian theorem -- Understanding the generative model -- Understanding prior distributions -- Introducing the likelihood function -- Introducing the posterior model…”

“…Ridge regression and LASSO are at the center of all penalty estimators in a range of standard models that are used in many applied statistical analyses. …”

“…This book is designed to guide you through using these libraries to implement effective statistical models for predictive analytics. You'll start by delving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- List of Figures -- Code Listings -- Preface -- About this Book -- Abstract -- Acknowledgments -- Introduction -- Chapter 1 Market Data -- 1.1 Tick and bar data -- 1.2 Corporate actions and adjustment factor -- 1.3 Linear vs log returns -- Chapter 2 Forecasting -- 2.1 Data for forecasts -- 2.1.1 Point‐in‐time and lookahead -- 2.1.2 Security master and survival bias -- 2.1.3 Fundamental and accounting data -- 2.1.4 Analyst estimates -- 2.1.5 Supply chain and competition -- 2.1.6 M&amp -- A and risk arbitrage -- 2.1.7 Event‐based predictors -- 2.1.8 Holdings and flows -- 2.1.9 News and social media -- 2.1.10 Macroeconomic data -- 2.1.11 Alternative data -- 2.1.12 Alpha capture -- 2.2 Technical forecasts -- 2.2.1 Mean reversion -- 2.2.2 Momentum -- 2.2.3 Trading volume -- 2.2.4 Statistical predictors -- 2.2.5 Data from other asset classes -- 2.3 Basic concepts of statistical learning -- 2.3.1 Mutual information and Shannon entropy -- 2.3.2 Likelihood and Bayesian inference -- 2.3.3 Mean square error and correlation -- 2.3.4 Weighted law of large numbers -- 2.3.5 Bias‐variance tradeoff -- 2.3.6 PAC learnability, VC dimension, and generalization error bounds -- 2.4 Machine learning -- 2.4.1 Types of machine learning -- 2.4.2 Overfitting -- 2.4.3 Ordinary and generalized least squares -- 2.4.4 Deep learning -- 2.4.5 Types of neural networks -- 2.4.6 Nonparametric methods -- 2.4.7 Hyperparameters -- 2.4.8 Cross‐validation -- 2.4.9 Convex regression -- 2.4.10 Curse of dimensionality, eigenvalue cleaning, and shrinkage -- 2.4.11 Smoothing and regularization -- 2.4.11.1 Smoothing spline. -- 2.4.11.2 Total variation denoising -- 2.4.11.3 Nadaraya-Watson kernel smoother -- 2.4.11.4 Local linear regression -- 2.4.11.5 Gaussian process -- 2.4.11.6 Ridge and kernel ridge regression…”

Tabla de Contenidos: “…Multinomial logistic regression -- Summary -- Chapter 5: Data Preparation Using R Tools -- Data wrangling -- A first look at data -- Change datatype -- Removing empty cells -- Replace incorrect value -- Missing values -- Treatment of NaN values -- Finding outliers in data -- Scale of features -- Min-max normalization -- z score standardization -- Discretization in R -- Data discretization by binning -- Data discretization by histogram analysis -- Dimensionality reduction -- Principal Component Analysis -- Summary -- Chapter 6: Avoiding Overfitting Problems - Achieving Generalization -- Understanding overfitting -- Overfitting detection - cross-validation -- Feature selection -- Stepwise regression -- Regression subset selection -- Regularization -- Ridge regression -- Lasso regression -- ElasticNet regression -- Summary -- Chapter 7: Going Further with Regression Models -- Robust linear regression -- Bayesian linear regression -- Basic concepts of probability -- Bayes' theorem -- Bayesian model using BAS package -- Count data model -- Poisson distributions -- Poisson regression model -- Modeling the number of warp breaks per loom -- Summary -- Chapter 8: Beyond Linearity - When Curving Is Much Better -- Nonlinear least squares -- Multivariate Adaptive Regression Splines -- Generalized Additive Model -- Regression trees -- Support Vector Regression -- Summary -- Chapter 9: Regression Analysis in Practice -- Random forest regression with the Boston dataset -- Exploratory analysis -- Multiple linear model fitting -- Random forest regression model -- Classifying breast cancer using logistic regression -- Exploratory analysis -- Model fitting -- Regression with neural networks -- Exploratory analysis -- Neural network model -- Summary -- Other Books You May Enjoy -- Index…”

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