Statistical Computing with R
Statistical Computing with R
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使用 R 进行统计计算
DOI：
10.1201/9780429192760
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Maria L. Rizzo
中科院分区：
文献类型：
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作者：
Maria L. Rizzo
前言引言计算统计和统计计算R环境R入门使用R联机帮助系统函数数组，数据帧，和使用脚本使用包列出工作区和文件图形概率和统计复习随机变量和概率一些离散分布一些连续分布多元正态分布极限定理统计贝叶斯定理和贝叶斯统计马尔可夫链方法随机变量的生成介绍逆变换法验收-拒绝法变换方法求和与混合多元分布随机过程练习多元数据可视化简介面板显示表面图和三维散点图等高线图其他二维数据表示法其他数据可视化方法练习蒙特卡罗积分和方差缩减简介蒙特卡罗积分方差缩减反向变量控制变量重要性抽样分层抽样分层重要性抽样练习R代码蒙特卡罗方法在推理介绍蒙特卡罗方法估计蒙特卡罗方法假设检验应用练习Bootstrap和Jackknife Bootstrap Jackknife后Bootstrap Bootstrap置信区间更好Bootstrap置信区间应用练习置换检验介绍相等的检验分布均匀分布的多元检验应用练习马尔可夫链蒙特卡罗方法介绍Metropolis-Hastings算法吉布斯采样器监测收敛应用练习R代码概率密度估计单变量密度估计核密度估计双变量和多变量密度估计其他方法密度估计练习R代码数值方法R介绍根-查找一维数值积分最大似然问题一维优化二维优化EM算法线性规划-单纯形法应用练习附录A：记法附录B：使用数据帧和数组恢复和数据分区子集化和整形数据数据输入和数据分析参考索引
preface Introduction Computational Statistics and Statistical Computing The R Environment Getting Started with R Using the R Online Help System Functions Arrays, Data Frames, and Lists Workspace and Files Using Scripts Using Packages Graphics Probability and Statistics Review Random Variables and Probability Some Discrete Distributions Some Continuous Distributions Multivariate Normal Distribution Limit Theorems Statistics Bayes' Theorem and Bayesian Statistics Markov Chains Methods for Generating Random Variables Introduction The Inverse Transform Method The Acceptance-Rejection Method Transformation Methods Sums and Mixtures Multivariate Distributions Stochastic Processes Exercises Visualization of Multivariate Data Introduction Panel Displays Surface Plots and 3D Scatter Plots Contour Plots Other 2D Representations of Data Other Approaches to Data Visualization Exercises Monte Carlo Integration and Variance Reduction Introduction Monte Carlo Integration Variance Reduction Antithetic Variables Control Variates Importance Sampling Stratified Sampling Stratified Importance Sampling Exercises R Code Monte Carlo Methods in Inference Introduction Monte Carlo Methods for Estimation Monte Carlo Methods for Hypothesis Tests Application Exercises Bootstrap and Jackknife The Bootstrap The Jackknife Jackknife-after-Bootstrap Bootstrap Confidence Intervals Better Bootstrap Confidence Intervals Application Exercises Permutation Tests Introduction Tests for Equal Distributions Multivariate Tests for Equal Distributions Application Exercises Markov Chain Monte Carlo Methods Introduction The Metropolis-Hastings Algorithm The Gibbs Sampler Monitoring Convergence Application Exercises R Code Probability Density Estimation Univariate Density Estimation Kernel Density Estimation Bivariate and Multivariate Density Estimation Other Methods of Density Estimation Exercises R Code Numerical Methods in R Introduction Root-Finding in One Dimension Numerical Integration Maximum Likelihood Problems 1D Optimization 2D Optimization The EM Algorithm Linear Programming-The Simplex Method Application Exercises APPENDIX A: Notation APPENDIX B: Working with Data Frames and Arrays Resampling and Data Partitioning Subsetting and Reshaping Data Data Entry and Data Analysis References Index