{"product_id":"computational-statistics-in-data-science-isbn-9781119561071","title":"Computational Statistics in Data Science","description":"\u003cp\u003e\u003cb\u003eAn essential roadmap to the application of computational statistics in contemporary data science\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eIn \u003ci\u003eComputational Statistics in Data Science\u003c\/i\u003e, a team of distinguished mathematicians and statisticians delivers an expert compilation of concepts, theories, techniques, and practices in computational statistics for readers who seek a single, standalone sourcebook on statistics in contemporary data science. The book contains multiple sections devoted to key, specific areas in computational statistics, offering modern and accessible presentations of up-to-date techniques. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eComputational Statistics in Data Science\u003c\/i\u003e provides complimentary access to finalized entries in the \u003ci\u003eWiley StatsRef: Statistics Reference Online\u003c\/i\u003e compendium. Readers will also find: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eA thorough introduction to computational statistics relevant and accessible to practitioners and researchers in a variety of data-intensive areas\u003c\/li\u003e \u003cli\u003eComprehensive explorations of active topics in statistics, including big data, data stream processing, quantitative visualization, and deep learning\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003ePerfect for researchers and scholars working in any field requiring intermediate and advanced computational statistics techniques, \u003ci\u003eComputational Statistics in Data Science \u003c\/i\u003ewill also earn a place in the libraries of scholars researching and developing computational data-scientific technologies and statistical graphics.Ein unverzichtbarer Leitfaden bei der Anwendung computergestützter Statistik in der modernen Datenwissenschaft\u003cbr\u003e \u003cbr\u003e In Computational Statistics in Data Science präsentiert ein Team aus bekannten Mathematikern und Statistikern eine fundierte Zusammenstellung von Konzepten, Theorien, Techniken und Praktiken der computergestützten Statistik für ein Publikum, das auf der Suche nach einem einzigen, umfassenden Referenzwerk für Statistik in der modernen Datenwissenschaft ist. Das Buch enthält etliche Kapitel zu den wesentlichen konkreten Bereichen der computergestützten Statistik, in denen modernste Techniken zeitgemäß und verständlich dargestellt werden.\u003cbr\u003e \u003cbr\u003e Darüber hinaus bietet Computational Statistics in Data Science einen kostenlosen Zugang zu den fertigen Einträgen im Online-Nachschlagewerk Wiley StatsRef: Statistics Reference Online. Außerdem erhalten die Leserinnen und Leser:\u003cbr\u003e * Eine gründliche Einführung in die computergestützte Statistik mit relevanten und verständlichen Informationen für Anwender und Forscher in verschiedenen datenintensiven Bereichen\u003cbr\u003e * Umfassende Erläuterungen zu aktuellen Themen in der Statistik, darunter Big Data, Datenstromverarbeitung, quantitative Visualisierung und Deep Learning\u003cbr\u003e \u003cbr\u003e Das Werk eignet sich perfekt für Forscher und Wissenschaftler sämtlicher Fachbereiche, die Techniken der computergestützten Statistik auf einem gehobenen oder fortgeschrittenen Niveau anwenden müssen. Zudem gehört Computational Statistics in Data Science in das Bücherregal von Wissenschaftlern, die sich mit der Erforschung und Entwicklung von Techniken der computergestützten Statistik und statistischen Grafiken beschäftigen. \u003c\/p\u003e\u003cp\u003eList of Contributors xxiii\u003c\/p\u003e \u003cp\u003ePreface xxix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\u003cbr\u003ePart I Computational Statistics and Data Science 1\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e\u003cb\u003e1 Computational Statistics and Data Science in the Twenty-first Century 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAndrew J. Holbrook, Akihiko Nishimura, Xiang Ji, and Marc A. Suchard\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 3\u003c\/p\u003e \u003cp\u003e2 Core Challenges 1–3 5\u003c\/p\u003e \u003cp\u003e3 Model-Specific Advances 8\u003c\/p\u003e \u003cp\u003e4 Core Challenges 4 and 5 12\u003c\/p\u003e \u003cp\u003e5 Rise of Data Science 16\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Statistical Software 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAlfred G. Schissler and Alexander D. Knudson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 User Development Environments 23\u003c\/p\u003e \u003cp\u003e2 Popular Statistical Software 26\u003c\/p\u003e \u003cp\u003e3 Noteworthy Statistical Software and Related Tools 30\u003c\/p\u003e \u003cp\u003e4 Promising and Emerging Statistical Software 36\u003c\/p\u003e \u003cp\u003e5 The Future of Statistical Computing 38\u003c\/p\u003e \u003cp\u003e6 Concluding Remarks 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\u003cbr\u003e3 An Introduction to Deep Learning Methods 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eYao Li, Justin Wang and Thomas C.M. Lee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 43\u003c\/p\u003e \u003cp\u003e2 Machine Learning: An Overview 43\u003c\/p\u003e \u003cp\u003e3 Feedforward Neural Networks 45\u003c\/p\u003e \u003cp\u003e4 Convolutional Neural Networks 48\u003c\/p\u003e \u003cp\u003e5 Autoencoders 52\u003c\/p\u003e \u003cp\u003e6 Recurrent Neural Networks 54\u003c\/p\u003e \u003cp\u003e7 Conclusion 57\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Streaming Data and Data Streams 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eTaiwo Kolajo, Olawande Daramola, and Ayodele Adebiyi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 59\u003c\/p\u003e \u003cp\u003e2 Data Stream Computing 61\u003c\/p\u003e \u003cp\u003e3 Issues in Data Stream Mining 61\u003c\/p\u003e \u003cp\u003e4 Streaming Data Tools and Technologies 64\u003c\/p\u003e \u003cp\u003e5 Streaming Data Pre-Processing: Concept and Implementation 65\u003c\/p\u003e \u003cp\u003e6 Streaming Data Algorithms 65\u003c\/p\u003e \u003cp\u003e7 Strategies for Processing Data Streams 68\u003c\/p\u003e \u003cp\u003e8 Best Practices for Managing Data Streams 69\u003c\/p\u003e \u003cp\u003e9 Conclusion and theWay Forward 70\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003ePart II Simulation-Based Methods 79\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Monte Carlo Simulation: Are We There Yet? 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eDootika Vats, James M. Flegal, and Galin L. Jones\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 81\u003c\/p\u003e \u003cp\u003e2 Estimation 83\u003c\/p\u003e \u003cp\u003e3 Sampling Distribution 84\u003c\/p\u003e \u003cp\u003e4 Estimating Σ 87\u003c\/p\u003e \u003cp\u003e5 Stopping Rules 88\u003c\/p\u003e \u003cp\u003e6 Workflow 89\u003c\/p\u003e \u003cp\u003e7 Examples 90\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Sequential Monte Carlo: Particle Filters and Beyond 99\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAdam M. Johansen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 99\u003c\/p\u003e \u003cp\u003e2 Sequential Importance Sampling and Resampling 99\u003c\/p\u003e \u003cp\u003e3 SMC in Statistical Contexts 106\u003c\/p\u003e \u003cp\u003e4 Selected Recent Developments 112\u003cbr\u003e\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Markov Chain Monte Carlo Methods, A Survey with Some Frequent Misunderstandings 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eChristian P. Robert and Wu Changye\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 119\u003c\/p\u003e \u003cp\u003e2 Monte Carlo Methods 121\u003c\/p\u003e \u003cp\u003e3 Markov Chain Monte Carlo Methods 128\u003c\/p\u003e \u003cp\u003e4 Approximate Bayesian Computation 141\u003c\/p\u003e \u003cp\u003e5 Further Reading 145\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e8 Bayesian Inference with Adaptive Markov Chain Monte Carlo 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMatti Vihola\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 151\u003c\/p\u003e \u003cp\u003e2 Random-Walk Metropolis Algorithm 151\u003c\/p\u003e \u003cp\u003e3 Adaptation of Random-Walk Metropolis 152\u003c\/p\u003e \u003cp\u003e4 Multimodal Targets with Parallel Tempering 156\u003c\/p\u003e \u003cp\u003e5 Dynamic Models with Particle Filters 157\u003c\/p\u003e \u003cp\u003e6 Discussion 159\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e9 Advances in Importance Sampling 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eVíctor Elvira and Luca Martino\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction and Problem Statement 165\u003c\/p\u003e \u003cp\u003e2 Importance Sampling 167\u003c\/p\u003e \u003cp\u003e3 Multiple Importance Sampling (MIS) 171\u003c\/p\u003e \u003cp\u003e4 Adaptive Importance Sampling (AIS) 174\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003ePart III Statistical Learning 183\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Supervised Learning 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eWeibin Mo and Yufeng Liu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 185\u003c\/p\u003e \u003cp\u003e2 Penalized Empirical Risk Minimization 186\u003c\/p\u003e \u003cp\u003e3 Linear Regression 190\u003c\/p\u003e \u003cp\u003e4 Classification 193\u003c\/p\u003e \u003cp\u003e5 Extensions for Complex Data 200\u003c\/p\u003e \u003cp\u003e6 Discussion 203\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e11 Unsupervised and Semisupervised Learning 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eJia Li and Vincent A. Pisztora\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 209\u003c\/p\u003e \u003cp\u003e2 Unsupervised Learning 210\u003c\/p\u003e \u003cp\u003e3 Semisupervised Learning 219\u003c\/p\u003e \u003cp\u003e4 Conclusions 224\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e12 Random Forest 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003ePeter Calhoun, Xiaogang Su, Kelly M. Spoon, Richard A. Levine, and Juanjuan Fan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 231\u003c\/p\u003e \u003cp\u003e2 Random Forest (RF) 232\u003c\/p\u003e \u003cp\u003e3 Random Forest Extensions 235\u003c\/p\u003e \u003cp\u003e4 Random Forests of Interaction Trees (RFIT) 239\u003c\/p\u003e \u003cp\u003e5 Random Forest of Interaction Trees for Observational Studies 243\u003c\/p\u003e \u003cp\u003e6 Discussion 249\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e13 Network Analysis 253\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eRong Ma and Hongzhe Li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 253\u003c\/p\u003e \u003cp\u003e2 Gaussian Graphical Models for Mixed Partial Compositional Data 255\u003c\/p\u003e \u003cp\u003e3 Theoretical Properties 257\u003c\/p\u003e \u003cp\u003e4 Graphical Model Selection 260\u003c\/p\u003e \u003cp\u003e5 Analysis of a Microbiome–Metabolomics Data 260\u003c\/p\u003e \u003cp\u003e6 Discussion 261\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e14 Tensors in Modern Statistical Learning 269\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eWill Wei Sun, Botao Hao, and Lexin Li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 269\u003c\/p\u003e \u003cp\u003e2 Background270\u003c\/p\u003e \u003cp\u003e3 Tensor Supervised Learning 272\u003c\/p\u003e \u003cp\u003e4 Tensor Unsupervised Learning 276\u003c\/p\u003e \u003cp\u003e5 Tensor Reinforcement Learning 282\u003c\/p\u003e \u003cp\u003e6 Tensor Deep Learning 286\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e15 Computational Approaches to Bayesian Additive Regression Trees 297\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eHugh Chipman, Edward George, Richard Hahn, Robert McCulloch, Matthew Pratola, and Rodney Sparapani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 297\u003c\/p\u003e \u003cp\u003e2 Bayesian CART 298\u003c\/p\u003e \u003cp\u003e3 TreeMCMC302\u003c\/p\u003e \u003cp\u003e4 The BART Model 308\u003c\/p\u003e \u003cp\u003e5 BART Example: Boston Housing Values and Air Pollution 310\u003c\/p\u003e \u003cp\u003e6 BARTMCMC311\u003c\/p\u003e \u003cp\u003e7 BART Extentions 313\u003c\/p\u003e \u003cp\u003e8 Conclusion 320 \u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003ePart IV High-Dimensional Data Analysis 323\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Penalized Regression 325\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eSeung Jun Shin and Yichao Wu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 325\u003c\/p\u003e \u003cp\u003e2 Penalization for Smoothness 326\u003c\/p\u003e \u003cp\u003e3 Penalization for Sparsity 328\u003c\/p\u003e \u003cp\u003e4 Tuning Parameter Selection 330\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e17 Model Selection in High-Dimensional Regression 333\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eHao H. Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Model Selection Problem 333\u003c\/p\u003e \u003cp\u003e2 Model Selection in High-Dimensional Linear Regression 335\u003c\/p\u003e \u003cp\u003e3 Interaction-Effect Selection for High-Dimensional Data 339\u003c\/p\u003e \u003cp\u003e4 Model Selection in High-Dimensional Nonparametric Models 342\u003c\/p\u003e \u003cp\u003e5 Concluding Remarks 349\u003cbr\u003e\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Sampling Local Scale Parameters in High-Dimensional Regression Models 355\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAnirban Bhattacharya and James E. Johndrow\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 355\u003c\/p\u003e \u003cp\u003e2 A Blocked Gibbs Sampler for the Horseshoe 356\u003c\/p\u003e \u003cp\u003e3 Sampling (𝜉, 𝜎2, 𝛽) 359\u003c\/p\u003e \u003cp\u003e4 Sampling 𝜂 360\u003c\/p\u003e \u003cp\u003e5 Appendix: A. Newton–Raphson Steps for the Inverse-cdf Sampler for 𝜂 367\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e19 Factor Modeling for High-Dimensional Time Series 371\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eChun Yip Yau\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 371\u003c\/p\u003e \u003cp\u003e2 Identifiability 372\u003c\/p\u003e \u003cp\u003e3 Estimation of High-Dimensional Factor Model 373\u003c\/p\u003e \u003cp\u003e4 Determining the Number of Factors 383\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Quantitative Visualization 387\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Visual Communication of Data: It Is Not a Programming Problem, It Is Viewer Perception 389\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eEdward Mulrow and Nola du Toit\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 389\u003c\/p\u003e \u003cp\u003e2 Case Studies Part 1 391\u003c\/p\u003e \u003cp\u003e3 Let StAR Be Your Guide 393\u003c\/p\u003e \u003cp\u003e4 Case Studies Part 2: Using StAR Principles to Develop Better Graphics 394\u003c\/p\u003e \u003cp\u003e5 Ask Colleagues Their Opinion 397\u003c\/p\u003e \u003cp\u003e6 Case Studies: Part 3 398\u003c\/p\u003e \u003cp\u003e7 Iterate 401\u003c\/p\u003e \u003cp\u003e8 Final Thoughts 402\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e21 Uncertainty Visualization 405\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eLace Padilla, Matthew Kay, and Jessica Hullman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 405\u003c\/p\u003e \u003cp\u003e2 Uncertainty Visualization Theories 408 \u003c\/p\u003e \u003cp\u003e3 General Discussion 420\u003cbr\u003e\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Big Data Visualization 427\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eLeland Wilkinson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 427\u003c\/p\u003e \u003cp\u003e2 Architecture for Big Data Analytics 428\u003c\/p\u003e \u003cp\u003e3 Filtering430\u003c\/p\u003e \u003cp\u003e4 Aggregating 430\u003c\/p\u003e \u003cp\u003e5 Analyzing 436 \u003c\/p\u003e \u003cp\u003e6 Big Data Graphics 436\u003c\/p\u003e \u003cp\u003e7 Conclusion 440\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\u003cbr\u003e23 Visualization-Assisted Statistical Learning 443\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eCatherine B. Hurley and Katarina Domijan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 443\u003c\/p\u003e \u003cp\u003e2 Better Visualizations with Seriation 444\u003c\/p\u003e \u003cp\u003e3 Visualizing Machine Learning Fits 445\u003c\/p\u003e \u003cp\u003e4 Condvis2 Case Studies 447\u003c\/p\u003e \u003cp\u003e5 Discussion 453\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e24 Functional Data Visualization 457\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMarc G. Genton and Ying Sun\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 457\u003c\/p\u003e \u003cp\u003e2 Univariate Functional Data Visualization 458\u003c\/p\u003e \u003cp\u003e3 Multivariate Functional Data Visualization 461\u003c\/p\u003e \u003cp\u003e4 Conclusions 465\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003ePart VI Numerical Approximation and Optimization 469\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Gradient-Based Optimizers for Statistics and Machine Learning 471\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eCho-Jui Hsieh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 471\u003c\/p\u003e \u003cp\u003e2 Convex Versus Nonconvex Optimization 472\u003c\/p\u003e \u003cp\u003e3 Gradient Descent 473\u003c\/p\u003e \u003cp\u003e4 Proximal Gradient Descent: Handling Nondifferentiable Regularization 475\u003c\/p\u003e \u003cp\u003e5 Stochastic Gradient Descent 476\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e26 Alternating Minimization Algorithms 481\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eDavid R. Hunter\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 481\u003c\/p\u003e \u003cp\u003e2 Coordinate Descent 482\u003c\/p\u003e \u003cp\u003e3 EM as Alternating Minimization 484\u003c\/p\u003e \u003cp\u003e3.1 Finite Mixture Models 485\u003c\/p\u003e \u003cp\u003e4 Matrix Approximation Algorithms 486\u003c\/p\u003e \u003cp\u003e5 Conclusion 489\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e27 A Gentle Introduction to Alternating Direction Method of Multipliers \u003c\/b\u003e\u003cb\u003e(ADMM) for Statistical Problems 493\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003ci\u003eShiqian Ma and Mingyi Hong\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 493\u003c\/p\u003e \u003cp\u003e2 Two Perfect Examples of ADMM 494\u003c\/p\u003e \u003cp\u003e3 Variable Splitting and Linearized ADMM 496\u003c\/p\u003e \u003cp\u003e4 Multiblock ADMM 499\u003c\/p\u003e \u003cp\u003e5 Nonconvex Problems 501\u003c\/p\u003e \u003cp\u003e6 Stopping Criteria 502\u003c\/p\u003e \u003cp\u003e7 Convergence Results of ADMM 502\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e28 Nonconvex Optimization via MM Algorithms: Convergence Theory 509\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eKenneth Lange, Joong-Ho Won, Alfonso Landeros, and Hua Zhou\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Background509\u003c\/p\u003e \u003cp\u003e2 Convergence Theorems 510\u003c\/p\u003e \u003cp\u003e3 Paracontraction 521\u003c\/p\u003e \u003cp\u003e4 Bregman Majorization 523\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003ePart VII High-Performance Computing 535\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e29 Massive Parallelization 537\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eRobert B. Gramacy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 537\u003c\/p\u003e \u003cp\u003e2 Gaussian Process Regression and Surrogate Modeling 539\u003c\/p\u003e \u003cp\u003e3 Divide-and-Conquer GP Regression 542\u003c\/p\u003e \u003cp\u003e4 Empirical Results 548\u003c\/p\u003e \u003cp\u003e5 Conclusion 552\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e30 Divide-and-Conquer Methods for Big Data Analysis 559\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eXueying Chen, Jerry Q. Cheng, and Min-ge Xie\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 559\u003c\/p\u003e \u003cp\u003e2 Linear Regression Model 560\u003c\/p\u003e \u003cp\u003e3 Parametric Models 561\u003c\/p\u003e \u003cp\u003e4 Nonparametric and Semiparametric Models 567\u003c\/p\u003e \u003cp\u003e5 Online Sequential Updating 568\u003c\/p\u003e \u003cp\u003e6 Splitting the Number of Covariates 569\u003c\/p\u003e \u003cp\u003e7 Bayesian Divide-and-Conquer and Median-Based Combining 570\u003c\/p\u003e \u003cp\u003e8 Real-World Applications 571\u003c\/p\u003e \u003cp\u003e9 Discussion 572\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e31 Bayesian Aggregation 577\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eYuling Yao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 From Model Selection to Model Combination 577\u003c\/p\u003e \u003cp\u003e2 From Bayesian Model Averaging to Bayesian Stacking 580\u003c\/p\u003e \u003cp\u003e3 Asymptotic Theories of Stacking 584\u003c\/p\u003e \u003cp\u003e4 Stacking in Practice 586\u003c\/p\u003e \u003cp\u003e5 Discussion 588\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003cb\u003e32 Asynchronous Parallel Computing 593\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMing Yan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 593\u003c\/p\u003e \u003cp\u003e2 Asynchronous Parallel Coordinate Update 597\u003c\/p\u003e \u003cp\u003e3 Asynchronous Parallel Stochastic Approaches 602\u003c\/p\u003e \u003cp\u003e4 Doubly Stochastic Coordinate Optimization with Variance Reduction 604\u003c\/p\u003e \u003cp\u003e5 Concluding Remarks 605\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eWALTER W. PIEGORSCH\u003c\/b\u003e is Professor of Mathematics at the University of Arizona and Director of Statistical Research \u0026amp; Education at the University’s BIO5 Institute. He is also a former Chair of the UArizona Interdisciplinary Program in Statistics, and a past editor of the \u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e (Theory \u0026amp; Methods Section). He is a fellow of the American Statistical Association and an elected member of the International Statistical Institute. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eRICHARD A. LEVINE\u003c\/b\u003e is Professor of Statistics at San Diego State University and Faculty Advisor overseeing the Statistical Modeling Group in SDSU Analytic Studies and Institutional Research. He is former Chair of the SDSU Department of Mathematics and Statistics and past Editor of the\u003ci\u003e Journal of Computational and Graphical Statistics\u003c\/i\u003e. He is Associate Editor for Statistics of the \u003ci\u003eNotices of the American Mathematical Society\u003c\/i\u003e and is a fellow of the American Statistical Association. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eHAO HELEN ZHANG\u003c\/b\u003e is Professor of Mathematics at the University of Arizona and Chair of the UArizona Interdisciplinary Program in Statistics. She is Editor-in-Chief of \u003ci\u003eSTAT\u003c\/i\u003e (the ISI journal) and Associate Editor of the \u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e and the \u003ci\u003eJournal of the Royal Statistical Society\u003c\/i\u003e. She is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eTHOMAS C. M. LEE\u003c\/b\u003e is Professor of Statistics and Associate Dean of the Faculty in Mathematical and Physical Sciences at the University of California, Davis. He is a former Chair of the Department of Statistics at the same institution and a past editor of the \u003ci\u003eJournal of Computational and Graphical Statistics\u003c\/i\u003e. He is an elected fellow of the American Association for the Advancement of Science, the American Statistical Association, and the Institute of Mathematical Statistics.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAn essential roadmap to the application of computational statistics in contemporary data science\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn \u003ci\u003eComputational Statistics in Data Science\u003c\/i\u003e, a team of distinguished mathematicians and statisticians delivers an expert compilation of concepts, theories, techniques, and practices in computational statistics for readers who seek a single, standalone sourcebook on statistics in contemporary data science. The book contains multiple sections devoted to key, specific areas in computational statistics, offering modern and accessible presentations of up-to-date techniques. \u003ci\u003eComputational Statistics in Data Science\u003c\/i\u003e reproduces finalized entries from the Wiley StatsRef: Statistics Reference Online compendium, collected and edited into a valuable standalone collection. Readers will also find: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA thorough introduction to computational statistics relevant and accessible to practitioners and researchers in a variety of data-intensive areas \u003c\/li\u003e \u003cli\u003eComprehensive explorations of active topics in statistics, including big data, data stream processing, quantitative visualization, and deep learning\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for researchers and scholars working in any field requiring intermediate and advanced computational statistics techniques, \u003ci\u003eComputational Statistics in Data Science\u003c\/i\u003e will also earn a place in the libraries of scholars researching and developing computational data-scientific technologies and statistical graphics.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988967604453,"sku":"NP9781119561071","price":215.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119561071.jpg?v=1761782246","url":"https:\/\/k12savings.com\/es\/products\/computational-statistics-in-data-science-isbn-9781119561071","provider":"K12savings","version":"1.0","type":"link"}