Elements of computational statistics

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<p>计算统计学和统计计算方法有着紧密的联系。本书论述包括数据集的重新采样、分类及多重变换，其中可能利用随机生成的人工数据等计算统计学方法。这些方法的运用需要数值分析的高等技巧。本书阐述计算统计学的各种方法以及集约计算方法在密度估计、数据结构的确认及模型的建立等各方面的一些应用，全面阐述了统计方法意义下的数据变换、函数近似及数据优化中的数值技巧。<br/></p><p> 185732.pdf (3.35 MB, 需要: 3 个论坛币)

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Elements of computational statistics
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关键词：Computation Statistics statistic Elements Element 计算方法 统计学 技巧 模型

目录

I Methods of Computational Statistics

Introduction to Part I
1 Preliminaries
1.1 Discovering Structure:Data Structures and Structiure in Data
1.2 Modeling and Computational Inference
1.3 The Role of the Empirical Cumulative Distribution Function
1.4 The Role of Optimization in Inference
1.5 Inference about Functions
1.6 Probability Statements in Statistical Inference
Excercises
2 Monte Carlo Methods for Statistical Inference
2.1 Generation of Random Nunbers
2.2 Monte Carlo Estimation 
2.3 Simulation of Data from a Hypothesized Model:monte Carlo Tests
2.4 Simulation of Data from a Fitted Model:"Parametric Bootstraps"
2.5 Random Sampling from Data
2.6 Reducing Variance in Monte Carlo Methods
2.7 Acceleration of Markov Chain Monte Chain Monte Carlo Methods
Exercises
3 Randomization and Data Partitioning
3.1 Randomixation Methods
3.2 Cross Validation for Smoothing and Fitting 
3.3 Jackknife Methods
Further Reading 
Exercises
4 Bootstrap Methods
4.1 Bootstrap Bias Corrections
4.2 Bootstrap Estimation of Variance
4.3 Bootstrap Confidence Intervals
4.4 Bootstrapping Data with Dependencies
4.5 Variance Reduction in Monte Carlo Bootstrap
Further Reading
Exercises
5 Tools for Identification of Structure in Data
5.1 Linear Structure and Other Geometric Properties
5.2 Linear Transformations
5.3 General Transformations of the Coordinate System
5.4 Measures of Similarity and Dissimilarity
5.5 Data Mining
5.6 Computational Feasibility
Exercises
6 Estimation of Functions
7 Graphical Methods in Computational Statistics

II Exploring Data Density and Structure

Introduction to Part II
8 Estimation of Probability Density Functions Using Parametric Models
9 Nonparametric Estimation of Probability Density Functions
10 Structure in data
11 Statistical Models of Dependencies
Appendices
A Monte Carlo Studies in Statistics
B Software for Randon Number Generation
C Notation and Definitions
D Solutions and Hints for Selected Exercises
Bibliography
Author Index
Subject Index

[此贴子已经被作者于2008-1-1 9:28:46编辑过]

Springer ebook

Elements of Computational Statistics

Series: Statistics and Computing
Gentle, James E.

Springer  1st. ed. 2002. Corr. 2nd printing, 2004, XVIII, 420 p. 86 illus., Hardcover

ISBN: 978-0-387-95489-9

About this book

Computationally intensive methods have become widely used both for statistical inference and for exploratory analyses of data. The methods of computational statistics involve resampling, partitioning, and multiple transformations of a dataset. They may also make use of randomly generated artificial data. Implementation of these methods often requires advanced techniques in numerical analysis, so there is a close connection between computational statistics and statistical computing. This book describes techniques used in computational statistics, and addresses some areas of application of computationally intensive methods, such as density estimation, identification of structure in data, and model building. Although methods of statistical computing are not emphasized in this book, numerical techniques for transformations, for function approximation, and for optimization are explained in the context of the statistical methods. The book includes exercises, some with solutions. The book can be used as a text or supplementary text for various courses in modern statistics at the advanced undergraduate or graduate level, and it can also be used as a reference for statisticians who use computationally-intensive methods of analysis. Although some familiarity with probability and statistics is assumed, the book reviews basic methods of inference, and so is largely self-contained. James Gentle is University Professor of Computational Statistics at George Mason University. He is a Fellow of the American Statistical Association and a member of the International Statistical Institute. He has held several national offices in the American Statistical Association and has served as associate editor for journals of the ASA as well as for other journals in statistics and computing. He is the author of Random Number Generation and Monte Carlo Methods and Numerical Linear Algebra for Statistical Applications.

Written for:

Graduate students, researchers

好书！

经典！！！

正找这本书呢！终于找到了，谢了！

好书，支持了

thanks

不错，价钱也厚道，最近买了两本书，差点穷了，坛子里通胀太厉害了，版主都不知道干什么去了，国家都知道干预啊！

这位副教授很厚道，书好，价格也合适。有些买家，书不匝地，还很贵。

谢谢