资源说明：一本基于matlab的数理统计电子书-Crc Press - Computational Statistics Handbook With Matlab -.part3.rar 希望对大家有所帮助:) Table of Contents Preface Chapter 1 Introduction 1.1 What Is Computational Statistics? 1.2 An Overview of the Book Philosophy What Is Covered A Word About Notation 1.3 MATLAB Code Computational Statistics Toolbox Internet Resources 1.4 Further Reading Chapter 2 Probability Concepts 2.1 Introduction 2.2 Probability Background Probability Axioms of Probability 2.3 Conditional Probability and Independence Conditional Probability Independence Bayes Theorem 2.4 Expectation Mean and Variance Skewness Kurtosis 2.5 Common Distributions Binomial Poisson Uniform Normal Exponential Gamma Chi-Square Weibull Beta Multivariate Normal 2.6 MATLAB Code 2.7 Further Reading Exercises Chapter 3 Sampling Concepts 3.1 Introduction 3.2 Sampling Terminology and Concepts Sample Mean and Sample Variance Sample Moments Covariance 3.3 Sampling Distributions 3.4 Parameter Estimation Bias Mean Squared Error Relative Efficiency Standard Error Maximum Likelihood Estimation Method of Moments 3.5 Empirical Distribution Function Quantiles 3.6 MATLAB Code 3.7 Further Reading Exercises Chapter 4 Generating Random Variables 4.1 Introduction 4.2 General Techniques for Generating Random Variables Uniform Random Numbers Inverse Transform Method Acceptance-Rejection Method 4.3 Generating Continuous Random Variables Normal Distribution Exponential Distribution Gamma Chi-Square Beta Multivariate Normal Generating Variates on a Sphere 4.4 Generating Discrete Random Variables Binomial Poisson Discrete Uniform 4.5 MATLAB Code 4.6 Further Reading Exercises Chapter 5 Exploratory Data Analysis 5.1 Introduction 5.2 Exploring Univariate Data Histograms Stem-and-Leaf Quantile-Based Plots - Continuous Distributions Q-Q Plot Quantile Plots Quantile Plots - Discrete Distributions Poissonness Plot Binomialness Plot Box Plots 5.3 Exploring Bivariate and Trivariate Data Scatterplots Surface Plots Contour Plots Bivariate Histogram 3-D Scatterplot 5.4 Exploring Multi-Dimensional Data Scatterplot Matrix Slices and Isosurfaces Star Plots Andrews Curves Parallel Coordinates Projection Pursuit Projection Pursuit Index Finding the Structure Structure Removal Grand Tour 5.5 MATLAB Code 5.6 Further Reading Exercises Chapter 6 Monte Carlo Methods for Inferential Statistics 6.1 Introduction 6.2 Classical Inferential Statistics Hypothesis Testing Confidence Intervals 6.3 Monte Carlo Methods for Inferential Statistics Basic Monte Carlo Procedure Monte Carlo Hypothesis Testing Monte Carlo Assessment of Hypothesis Testing 6.4 Bootstrap Methods General Bootstrap Methodology Bootstrap Estimate of Standard Error Bootstrap Estimate of Bias Bootstrap Confidence Intervals Bootstrap Standard Confidence Interval Bootstrap-t Confidence Interval Bootstrap Percentile Interval 6.5 MATLAB Code 6.6 Further Reading Exercises Chapter 7 Data Partitioning 7.1 Introduction 7.2 Cross-Validation 7.3 Jackknife 7.4 Better Bootstrap Confidence Intervals 7.5 Jackknife-After-Bootstrap 7.6 MATLAB Code 7.7 Further Reading Exercises Chapter 8 Probability Density Estimation 8.1 Introduction 8.2 Histograms 1-D Histograms Multivariate Histograms Frequency Polygons Averaged Shifted Histograms 8.3 Kernel Density Estimation Univariate Kernel Estimators Multivariate Kernel Estimators 8.4 Finite Mixtures Univariate Finite Mixtures Visualizing Finite Mixtures Multivariate Finite Mixtures EM Algorithm for Estimating the Parameters Adaptive Mixtures 8.5 Generating Random Variables 8.6 MATLAB Code 8.7 Further Reading Exercises Chapter 9 Statistical Pattern Recognition 9.1 Introduction 9.2 Bayes Decision Theory Estimating Class-Conditional Probabilities: Parametric Method Estimating Class-Conditional Probabilities: Nonparametric Bayes Decision Rule Likelihood Ratio Approach 9.3 Evaluating the Classifier Independent Test Sample Cross-Validation Receiver Operating Characteristic Curve 9.4 Classification Trees Growing the Tree Pruning the Tree Choosing the Best Tree Selecting the Best Tree Using an Independent Test Sample Selecting the Best Tree Using Cross-Validation 9.5 Clustering Measures of Distance Hierarchical Clustering K-Means Clustering 9.6 MATLAB Code 9.7 Further Reading Exercises Chapter 10 Nonparametric Regression 10.1 Introduction 10.2 Smoothing Loess Robust Loess Smoothing Upper and Lower Smooths 10.3 Kernel Methods Nadaraya-Watson Estimator Local Linear Kernel Estimator 10.4 Regression Trees Growing a Regression Tree Pruning a Regression Tree Selecting a Tree 10.5 MATLAB Code 10.6 Further ReadingExercises Chapter 11 Markov Chain Monte Carlo Methods 11.1 Introduction 11.2 Background Bayesian Inference Monte Carlo Integration Markov Chains Analyzing the Output 11.3 Metropolis-Hastings Algorithms Metropolis-Hastings Sampler Metropolis Sampler Independence Sampler Autoregressive Generating Density 11.4 The Gibbs Sampler 11.5 Convergence Monitoring Gelman and Rubin Method Raftery and Lewis Method 11.6 MATLAB Code 11.7 Further Reading Exercises Chapter 12 Spatial Statistics 12.1 Introduction What Is Spatial Statistics? Types of Spatial Data Spatial Point Patterns Complete Spatial Randomness 12.2 Visualizing Spatial Point Processes 12.3 Exploring First-order and Second-order Properties Estimating the Intensity Estimating the Spatial Dependence Nearest Neighbor Distances - G and F Distributions K-Function 12.4 Modeling Spatial Point Processes Nearest Neighbor Distances K-Function 12.5 Simulating Spatial Point Processes Homogeneous Poisson Process Binomial Process Poisson Cluster Process Inhibition Process Strauss Process 12.6 MATLAB Code 12.7 Further Reading Exercises Appendix A Introduction to MATLAB A.1 What Is MATLAB? A.2 Getting Help in MATLAB A.3 File and Workspace Management A.4 Punctuation in MATLAB A.5 Arithmetic Operators A.6 Data Constructs in MATLAB Basic Data Constructs Building Arrays Cell Arrays A.7 Script Files and Functions A.8 Control Flow For Loop While Loop If-Else Statements Switch Statement A.9 Simple Plotting A.10 Contact Information Appendix B Index of Notation Appendix C Projection Pursuit Indexes C.1 Indexes Friedman-Tukey Index Entropy Index Moment Index Distances C.2 MATLAB Source Code Appendix D MATLAB Code D.1 Bootstrap Confidence Interval D.2 Adaptive Mixtures Density Estimation D.3 Classification Trees D.4 Regression Trees Appendix E MATLAB Statistics Toolbox Appendix F Computational Statistics Toolbox Appendix G Data Sets References

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