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计算统计学基础
  • 书号:9787030166869
    作者:(美)金特尔(Gentle,J.E.)
  • 外文书名:
  • 装帧:圆脊精装
    开本:B5
  • 页数:422
    字数:515000
    语种:en
  • 出版社:科学出版社
    出版时间:2006-01-01
  • 所属分类:
  • 定价: ¥178.00元
    售价: ¥140.62元
  • 图书介质:
    纸质书

  • 购买数量: 件  商品库存: 2
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目录

  • Contents
    Preface v
    1 Methods of Computational Statistics 1
    Introduction to Part 1 3
    1 Preliminaries 5
    1.1 Discovering Structure: Data Structures and Structure in Data 6
    1.2 Modeling and Computational Inference 8
    1.3 The Role of the Empirical Cumulative Distribution Function 11
    1.4 The Role of Optimization in Inference 15
    1.5 Inference about Functions 30
    1.6 Probability Statements in Statistical Inference 32
    Exercises 35
    2 Monte Carlo Methods for Statistical Inference 39
    2.1 Generation of Random Numbers 40
    2.2 Monte Carlo Estimation 53
    2.3 Simulation of Data from a Hypothesized Model:
    Monte Carlo Tests 58
    2.4 Simulation of Data from a Fitted Model:"Parametric Bootstraps" 60
    2.5 Random Sampling from Data 60
    2.6 Reducing Variance in Monte Carlo Methods 61
    2.7 Acceleration of Markov Chain Monte Carlo Methods 65
    Exercises 66
    3 Randomization and Data Partitioning 69
    3.1 Randomization Methods 70
    3.2 Cross Validation for Smoothing and Fitting 74
    3.3 Jackknife Methods 76
    Further Reading 82
    Exercises 83
    4 Bootstrap Methods 85
    4.1 Bootstrap Bias Corrections 86
    4.2 Bootstrap Estimation of Variance 88
    4.3 Bootstrap Confidence Intervals 89
    4.4 Bootstrapping Data with Dependencies 93
    4.5 Variance Reduction in Monte Carlo Bootstrap 94
    Further Reading 96
    Exercises 97
    5 Tools for Identification of Structure in Data 99
    5.1 Linear Structure and Other Geometric Properties 100
    5.2 Linear Transformations 101
    5.3 General Transformations of the Coordinate System 108
    5.4 Measures of Similarity and Dissimilarity 109
    5.5 Data Mining 123
    5.6 Computational Feasibility 124
    Exercises 125
    6 Estimation of Functions 127
    6.1 General Methods for Estimating Functions 128
    6.2 Pointwise Properties of Function Estimators 143
    6.3 Global Properties of Estimators of Functions 146
    Exercises 150
    7 Graphical Methods in Computational Statistics 153
    7.1 Viewing One, Two, or Three Variables 155
    7.2 Viewing Multivariate Data 168
    7.3 Hardware and Low-Level Software for Graphics 184
    7.4 Software for Graphics Applications 186
    Further Reading 188
    Exercises 188
    II Exploring Data Density and Structure 191
    Introduction to Part 11 193
    8 Estimation of Probability Density Functions Using Parametric Models 197
    8.1 Fitting a Parametric Probability Distribution 198
    8.2 General Families of Probability Distributions 199
    8.3 Mixtures of Parametric Families 202
    Exercises 203
    9 Nonparametric Estimation of Probability Density Functions 205
    9.1 The Likelihood Function 206
    9.2 Histogram Estimators 208
    9.3 Kernel Estimators 217
    9.4 Choice of Window Widths 222
    9.5 Orthogonal Series Estimators 222
    9.6 Other Methods of Density Estimation 224
    Exercises 226
    10 Structure in Data 233
    10.1 Clustering and Classification 237
    10.2 Ordering and Ranking Multivariate Data 255
    10.3 Linear Principal Components 264
    10 .4 Variants of Principal Components 276
    10.5 Projection Pursuit 281
    10.6 Other Methods for Identifying Structure 289
    10.7 Higher Dimensions 290
    Exercises 294
    11 Statistical Models of Dependencies 299
    11. 1 Regression and Classificat ion Models 301
    11.2 Probability Distributions in Models 308
    11.3 Fitting Models to Data 311
    Exercises 333
    Appendices 336
    A Monte Carlo Studies in Statistics 337
    A.1 Simulation as an Experiment 338
    A.2 Reporting Simulation Experiments 339
    A.3 An Example 340
    A.4 Computer Experiments 347
    Exercises 349
    B Software for Random Number Generation 351
    B.1 The User Interface for Random Number Gencrators 353
    B.2 Controlling the Seeds in Monte Carlo Studies 354
    B.3 Random Number Generation in IMSL Libraries 354
    B.4 Random Number Generat ion in S-Plus and R 357
    C Notation and Definitions 363
    D Solutions and Hints for Selected Exercises 377
    Bibliography 385
    Literature in Computational Statistics 386
    Resources A vailable over the Internet 387
    References for Software Packages 389
    References to the Literature 389
    Author Index 409
    Subject Index 415
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