{"product_id":"computational-statistics-isbn-9780470533314","title":"Computational Statistics","description":"\u003cp\u003eThis new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing.  The book is comprised of four main parts spanning the field:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eOptimization\u003c\/li\u003e \u003cli\u003eIntegration and Simulation\u003c\/li\u003e \u003cli\u003eBootstrapping\u003c\/li\u003e \u003cli\u003eDensity Estimation and Smoothing\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWithin these sections,each chapter includes a comprehensive introduction and step-by-step implementation summaries to accompany the explanations of key methods.  The new edition includes updated coverage and existing topics as well as new topics such as adaptive MCMC and bootstrapping for correlated data.  The book website now includes comprehensive R code for the entire book.  There are extensive exercises, real examples, and helpful insights about how to use the methods in practice.\u003c\/p\u003e  \u003cp\u003ePREFACE xv\u003c\/p\u003e \u003cp\u003eACKNOWLEDGMENTS xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1\u003c\/b\u003e \u003cb\u003eREVIEW\u003c\/b\u003e \u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Mathematical Notation 1\u003c\/p\u003e \u003cp\u003e1.2 Taylor’s Theorem and Mathematical Limit Theory 2\u003c\/p\u003e \u003cp\u003e1.3 Statistical Notation and Probability Distributions 4\u003c\/p\u003e \u003cp\u003e1.4 Likelihood Inference 9\u003c\/p\u003e \u003cp\u003e1.5 Bayesian Inference 11\u003c\/p\u003e \u003cp\u003e1.6 Statistical Limit Theory 13\u003c\/p\u003e \u003cp\u003e1.7 Markov Chains 14\u003c\/p\u003e \u003cp\u003e1.8 Computing 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I\u003c\/b\u003e \u003cb\u003eOPTIMIZATION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2\u003c\/b\u003e \u003cb\u003eOPTIMIZATION AND SOLVING NONLINEAR EQUATIONS\u003c\/b\u003e \u003cb\u003e21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Univariate Problems 22\u003c\/p\u003e \u003cp\u003e2.2 Multivariate Problems 34\u003c\/p\u003e \u003cp\u003eProblems 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3\u003c\/b\u003e \u003cb\u003eCOMBINATORIAL OPTIMIZATION\u003c\/b\u003e \u003cb\u003e59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Hard Problems and NP-Completeness 59\u003c\/p\u003e \u003cp\u003e3.2 Local Search 65\u003c\/p\u003e \u003cp\u003e3.3 Simulated Annealing 68\u003c\/p\u003e \u003cp\u003e3.4 Genetic Algorithms 75\u003c\/p\u003e \u003cp\u003e3.5 Tabu Algorithms 85\u003c\/p\u003e \u003cp\u003eProblems 92\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4\u003c\/b\u003e \u003cb\u003eEM OPTIMIZATION METHODS\u003c\/b\u003e \u003cb\u003e97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Missing Data, Marginalization, and Notation 97\u003c\/p\u003e \u003cp\u003e4.2 The EM Algorithm 98\u003c\/p\u003e \u003cp\u003e4.3 EM Variants 111\u003c\/p\u003e \u003cp\u003eProblems 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II\u003c\/b\u003e \u003cb\u003eINTEGRATION AND SIMULATION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5\u003c\/b\u003e \u003cb\u003eNUMERICAL INTEGRATION\u003c\/b\u003e \u003cb\u003e129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Newton–Côtes Quadrature 129\u003c\/p\u003e \u003cp\u003e5.2 Romberg Integration 139\u003c\/p\u003e \u003cp\u003e5.3 Gaussian Quadrature 142\u003c\/p\u003e \u003cp\u003e5.4 Frequently Encountered Problems 146\u003c\/p\u003e \u003cp\u003eProblems 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6\u003c\/b\u003e \u003cb\u003eSIMULATION AND MONTE CARLO INTEGRATION\u003c\/b\u003e \u003cb\u003e151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction to the Monte Carlo Method 151\u003c\/p\u003e \u003cp\u003e6.2 Exact Simulation 152\u003c\/p\u003e \u003cp\u003e6.3 Approximate Simulation 163\u003c\/p\u003e \u003cp\u003e6.4 Variance Reduction Techniques 180\u003c\/p\u003e \u003cp\u003eProblems 195\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7\u003c\/b\u003e \u003cb\u003eMARKOV CHAIN MONTE CARLO\u003c\/b\u003e \u003cb\u003e201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Metropolis–Hastings Algorithm 202\u003c\/p\u003e \u003cp\u003e7.2 Gibbs Sampling 209\u003c\/p\u003e \u003cp\u003e7.3 Implementation 218\u003c\/p\u003e \u003cp\u003eProblems 230\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8\u003c\/b\u003e \u003cb\u003eADVANCED TOPICS IN MCMC\u003c\/b\u003e \u003cb\u003e237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Adaptive MCMC 237\u003c\/p\u003e \u003cp\u003e8.2 Reversible Jump MCMC 250\u003c\/p\u003e \u003cp\u003e8.3 Auxiliary Variable Methods 256\u003c\/p\u003e \u003cp\u003e8.4 Other Metropolis–Hastings Algorithms 260\u003c\/p\u003e \u003cp\u003e8.5 Perfect Sampling 264\u003c\/p\u003e \u003cp\u003e8.6 Markov Chain Maximum Likelihood 268\u003c\/p\u003e \u003cp\u003e8.7 Example: MCMC for Markov Random Fields 269\u003c\/p\u003e \u003cp\u003eProblems 279\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III\u003c\/b\u003e \u003cb\u003eBOOTSTRAPPING\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9\u003c\/b\u003e \u003cb\u003eBOOTSTRAPPING\u003c\/b\u003e \u003cb\u003e287\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 The Bootstrap Principle 287\u003c\/p\u003e \u003cp\u003e9.2 Basic Methods 288\u003c\/p\u003e \u003cp\u003e9.3 Bootstrap Inference 292\u003c\/p\u003e \u003cp\u003e9.4 Reducing Monte Carlo Error 302\u003c\/p\u003e \u003cp\u003e9.5 Bootstrapping Dependent Data 303\u003c\/p\u003e \u003cp\u003e9.6 Bootstrap Performance 315\u003c\/p\u003e \u003cp\u003e9.7 Other Uses of the Bootstrap 316\u003c\/p\u003e \u003cp\u003e9.8 Permutation Tests 317\u003c\/p\u003e \u003cp\u003eProblems 319\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART IV\u003c\/b\u003e \u003cb\u003eDENSITY ESTIMATION AND SMOOTHING\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10\u003c\/b\u003e \u003cb\u003eNONPARAMETRIC DENSITY ESTIMATION\u003c\/b\u003e \u003cb\u003e325\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Measures of Performance 326\u003c\/p\u003e \u003cp\u003e10.2 Kernel Density Estimation 327\u003c\/p\u003e \u003cp\u003e10.3 Nonkernel Methods 341\u003c\/p\u003e \u003cp\u003e10.4 Multivariate Methods 345\u003c\/p\u003e \u003cp\u003eProblems 359\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11\u003c\/b\u003e \u003cb\u003eBIVARIATE SMOOTHING\u003c\/b\u003e \u003cb\u003e363\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Predictor–Response Data 363\u003c\/p\u003e \u003cp\u003e11.2 Linear Smoothers 365\u003c\/p\u003e \u003cp\u003e11.3 Comparison of Linear Smoothers 377\u003c\/p\u003e \u003cp\u003e11.4 Nonlinear Smoothers 379\u003c\/p\u003e \u003cp\u003e11.5 Confidence Bands 384\u003c\/p\u003e \u003cp\u003e11.6 General Bivariate Data 388\u003c\/p\u003e \u003cp\u003eProblems 389\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12\u003c\/b\u003e \u003cb\u003eMULTIVARIATE SMOOTHING\u003c\/b\u003e \u003cb\u003e393\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Predictor–Response Data 393\u003c\/p\u003e \u003cp\u003e12.2 General Multivariate Data 413\u003c\/p\u003e \u003cp\u003eProblems 416\u003c\/p\u003e \u003cp\u003e\u003cb\u003eDATA ACKNOWLEDGMENTS\u003c\/b\u003e \u003cb\u003e421\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eREFERENCES\u003c\/b\u003e \u003cb\u003e423\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eINDEX\u003c\/b\u003e \u003cb\u003e457\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eGEOF H. GIVENS, PhD,\u003c\/b\u003e is Associate Professor in the Department of Statistics at Colorado State University. He serves as Associate Editor for \u003ci\u003eComputational Statistics and Data Analysis.\u003c\/i\u003e His research interests include statistical problems in wildlife conservation biology including ecology, population modeling and management, and automated computer face recognition.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eJENNIFER A. HOETING, PhD,\u003c\/b\u003e is Professor in the Department of Statistics at Colorado State University. She is an award-winning teacher who co-leads large research efforts for the National Science Foundation. She has served as associate editor for the \u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e and \u003ci\u003eEnvironmetrics.\u003c\/i\u003e Her research interests include spatial statistics, Bayesian methods, and model selection.\u003c\/p\u003e \u003cp\u003eGivens and Hoeting have taught graduate courses on computational statistics for nearly twenty years, and short courses to leading statisticians and scientists around the world.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eA valuable new edition of the complete guide to modern statistical computing\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eComputational Statistics, Second Edition\u003c\/i\u003e continues to serve as a comprehensive guide to the theory and practice of statistical computing. Like its predecessor, the new edition spans a broad range of modern and classic topics including optimization, integration, Monte Carlo methods, bootstrapping, density estimation and smoothing. Algorithms are explained both conceptually and by using step-by-step descriptions, and are illustrated with detailed examples and exercises.\u003c\/p\u003e \u003cp\u003eImportant features of this \u003ci\u003eSecond Edition\u003c\/i\u003e include:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eExamples based on real-world applications from various fields including genetics, ecology, economics, network systems, biology, and medicine\u003c\/li\u003e \u003cli\u003eExplanations of how computational methods are important components of major statistical approaches such as Bayesian models, linear and generalized linear models, random effects models, survival models, and hidden Markov models\u003c\/li\u003e \u003cli\u003eExpanded coverage of Markov chain Monte Carlo methods\u003c\/li\u003e \u003cli\u003eNew topics such as sequential sampling methods, particle filters, derivative free optimization, bootstrapping dependent data, and adaptive MCMC\u003c\/li\u003e \u003cli\u003eNew exercises and examples that help readers develop the skills needed to apply computational methods to a broad array of statistical problems\u003c\/li\u003e \u003cli\u003eA companion website offering datasets and code in the R software package\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eComputational Statistics, Second Edition\u003c\/i\u003e is perfect for advanced undergraduate or graduate courses in statistical computing and as a reference for practicing statisticians.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988967473381,"sku":"NP9780470533314","price":142.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470533314.jpg?v=1761782246","url":"https:\/\/k12savings.com\/es\/products\/computational-statistics-isbn-9780470533314","provider":"K12savings","version":"1.0","type":"link"}