000			05386cam a22002774a 4500
008			100227s2008 000 0 eng
010			_a 2007034218
020			_a 9781584885450 (alk. paper)
020			_a 1584885459 (alk. paper)
035			_a(Sirsi) u3886
040			_aEG-CaNU _C EG-CaNU _C EG-CaNU
042			_ancode
082	0	0	_a519.502855133 _2 22
100	1		_aRizzo, Maria L. _94470
245	1	0	_aStatistical computing with R / _c Maria L. Rizzo.
260			_a Boca Raton : _b Chapman & Hall/CRC, _c c2008.
300			_axvi, 399 p. : _b ill. ; _c 25 cm.
490	1		_aChapman & Hall/CRC computer science and data analysis series
504			_a Includes bibliographical references (p. 375-393) and index.
505			_aPrefacep. xv Introductionp. 1 Computational Statistics and Statistical Computingp. 1 The R Environmentp. 3 Getting Started with Rp. 4 Using the R Online Help Systemp. 7 Functionsp. 8 Arrays, Data Frames, and Listsp. 9 Workspace and Filesp. 15 Using Scriptsp. 17 Using Packagesp. 18 Graphicsp. 19 Probability and Statistics Reviewp. 21 Random Variables and Probabilityp. 21 Some Discrete Distributionsp. 25 Some Continuous Distributionsp. 29 Multivariate Normal Distributionp. 33 Limit Theoremsp. 35 Statisticsp. 35 Bayes' Theorem and Bayesian Statisticsp. 40 Markov Chainsp. 42 Methods for Generating Random Variablesp. 47 Introductionp. 47 The Inverse Transform Methodp. 49 The Acceptance-Rejection Methodp. 55 Transformation Methodsp. 58 Sums and Mixturesp. 61 Multivariate Distributionsp. 69 Stochastic Processesp. 82 Exercisesp. 94 Visualization of Multivariate Datap. 97 Introductionp. 97 Panel Displaysp. 97 Surface Plots and 3D Scatter Plotsp. 100 Contour Plotsp. 106 Other 2D Representations of Datap. 110 Other Approaches to Data Visualizationp. 115 Exercisesp. 116 Monte Carlo Integration and Variance Reductionp. 119 Introductionp. 119 Monte Carlo Integrationp. 119 Variance Reductionp. 126 Antithetic Variablesp. 128 Control Variatesp. 132 Importance Samplingp. 139 Stratified Samplingp. 144 Stratified Importance Samplingp. 147 Exercisesp. 149 R Codep. 152 Monte Carlo Methods in Inferencep. 153 Introductionp. 153 Monte Carlo Methods for Estimationp. 154 Monte Carlo Methods for Hypothesis Testsp. 162 Applicationp. 174 Exercisesp. 180 Bootstrap and Jackknifep. 183 The Bootstrapp. 183 The Jackknifep. 190 Jackknife-after-Bootstrapp. 195 Bootstrap Confidence Intervalsp. 197 Better Bootstrap Confidence Intervalsp. 203 Applicationp. 207 Exercisesp. 212 Permutation Testsp. 215 Introductionp. 215 Tests for Equal Distributionsp. 219 Multivariate Tests for Equal Distributionsp. 222 Applicationp. 235 Exercisesp. 242 Markov Chain Monte Carlo Methodsp. 245 Introductionp. 245 The Metropolis-Hastings Algorithmp. 247 The Gibbs Samplerp. 263 Monitoring Convergencep. 266 Applicationp. 271 Exercisesp. 277 R Codep. 279 Probability Density Estimationp. 281 Univariate Density Estimationp. 281 Kernel Density Estimationp. 296 Bivariate and Multivariate Density Estimationp. 305 Other Methods of Density Estimationp. 314 Exercisesp. 314 R Codep. 317 Numerical Methods in Rp. 319 Introductionp. 319 Root-finding in One Dimensionp. 326 Numerical Integrationp. 330 Maximum Likelihood Problemsp. 335 One-dimensional Optimizationp. 338 Two-dimensional Optimizationp. 342 The EM Algorithmp. 345 Linear Programming - The Simplex Methodp. 348 Applicationp. 349 Exercisesp. 353 Notationp. 355 Working with Data Frames and Arraysp. 357 Resampling and Data Partitioningp. 357 Subsetting and Reshaping Datap. 360 Data Entry and Data Analysisp. 364 Referencesp. 375 Indexp. 395 Table of Contents provided by Ingram. All Rights Reserved.
520			_aComputational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. One of the first books on these topics to feature R, Statistical Computing with R covers the traditional core material of computational statistics, with an emphasis on using the R language via an examples-based approach. Suitable for an introductory course in computational statistics or for self-study, it includes R code for all examples and R notes to help explain the R programming concepts. After an overview of computational statistics and an introduction to the R computing environment, the book reviews some basic concepts in probability and classical statistical inference. Each subsequent chapter explores a specific topic in computational statistics. These chapters cover the simulation of random variables from probability distributions, the visualization of multivariate data, Monte Carlo integration and variance reduction methods, Monte Carlo methods in inference, bootstrap and jackknife, permutation tests, Markov chain Monte Carlo (MCMC) methods, and density estimation. The final chapter presents a selection of examples that illustrate the application of numerical methods using R functions. Focusing on implementation rather than theory, this text serves as a balanced, accessible introduction to computational statistics and statistical computing.
650		0	_aMathematical statistics _x Data processing. _97656
650		0	_aStatistics _x Data processing. _97657
650		0	_aR (Computer program language) _92301
596			_a1
999			_c2866 _d2866