Time Series Analysis With Applications in R, Second Edition, presents an accessible approach to understanding time series models and their applications. Although the emphasis is on time domain ARIMA models and their analysis, the new edition devotes two chapters to the frequency domain and three to time series regression models, models for heteroscedasticity, and threshold models. All of the ideas and methods are illustrated with both real and simulated data sets.A unique feature of this edition is its integration with the R computing environment. The tables and graphical displays are accompanied by the R commands used to produce them. An extensive R package, TSA, which contains many new or revised R functions and all of the data used in the book, accompanies the written text. Script files of R commands for each chapter are available for download. There is also an extensive appendix in the book that leads the reader through the use of R commands and the new R package to carryout the analyses.

zlib/no-category/克莱尔/华章数学译丛49 时间序列分析及应用 R语言(中文版.原书第2版)_27022537.pdf

Time Series Analysis: With Applications In R (springer Texts In Statistics)

time series analysis and application (R language) (formerly Book 2)

时间序列分析及应用 : R语言 = Time series analysis with applications in R

Shi jian xu lie fen xi ji ying yong: R Yu yan

时间序列分析及应用 R语言 原书第2版

(Mei)Jonathan D. Cryer, (mei)Kung-Sik Chan zhu; pan hong yu deng yi

Jonathan D. Cryer; 克莱尔; An Ch; Kung-Sik Chan; 潘红宇

(MEI )KE LAI ER DENG PAN HONG YU DENG YI

Laier Ke, An Ch, Hongyu Pan

克莱尔 (Cryer, Jonathan D.)

Machinery Industry Press Pub. Date :2011-01-01

Academic Press, Incorporated

Ji xie gong ye chu ban she

Morgan Kaufmann Publishers

China Machine Press

Springer US

Brooks/Cole

Copernicus

Telos

Springer texts in statistics, 2. ed., corr. at 3. print, New York, NY, 2009

Springer texts in statistics, 2nd ed., New York, New York State, 2008

Hua zhang shu xue yi cong ;v49, Di 1 ban, Beijing, China, 2011

Springer texts in statistics, Second edition, New York, 2008

United States, United States of America

华章数学译丛, Di 1 ban, Beijing, 2011

China, People's Republic, China

2nd, PS, 2008

2nd, 1991

2, 2010

Includes bibliographical references (p. 477-486) and index.

Bookmarks: p1 (p1): 第1章 引论
p1-1 (p1): 1.1时间序列举例
p1-2 (p6): 1.2建模策略
p1-3 (p6): 1.3历史上的时间序列图
p1-4 (p7): 1.4本书概述
p1-5 (p7): 习题
p2 (p8): 第2章 基本概念
p2-1 (p8): 2.1时间序列与随机过程
p2-2 (p8): 2.2均值、方差和协方差
p2-3 (p11): 2.3平稳性
p2-4 (p14): 2.4小结
p2-5 (p14): 习题
p2-6 (p18): 附录A期望、方差、协方差和相关系数
p3 (p20): 第3章 趋势
p3-1 (p20): 3.1确定性趋势与随机趋势
p3-2 (p20): 3.2常数均值的估计
p3-3 (p22): 3.3回归方法
p3-4 (p26): 3.4回归估计的可靠性和有效性
p3-5 (p29): 3.5回归结果的解释
p3-6 (p31): 3.6残差分析
p3-7 (p36): 3.7小结
p3-8 (p37): 习题
p4 (p40): 第4章 平稳时间序列模型
p4-1 (p40): 4.1一般线性过程
p4-2 (p41): 4.2滑动平均过程
p4-3 (p48): 4.3自回归过程
p4-4 (p56): 4.4自回归滑动平均混合模型
p4-5 (p57): 4.5可逆性
p4-6 (p58): 4.6小结
p4-7 (p58): 习题
p4-8 (p61): 附录B AR（2）过程的平稳域
p4-9 (p62): 附录C ARMA（p,q）模型的自相关函数
p5 (p63): 第5章 非平稳时间序列模型
p5-1 (p63): 5.1通过差分平稳化
p5-2 (p66): 5.2 ARIMA模型
p5-3 (p70): 5.3 ARIMA模型中的常数项
p5-4 (p70): 5.4其他变换
p5-5 (p73): 5.5小结
p5-6 (p73): 习题
p5-7 (p75): 附录D延迟算子
p6 (p77): 第6章 模型识别
p6-1 (p77): 6.1样本自相关函数的性质
p6-2 (p79): 6.2偏自相关函数和扩展的自相关函数
p6-3 (p83): 6.3对一些模拟的时间序列数据的识别
p6-4 (p88): 6.4非平稳性
p6-5 (p92): 6.5其他识别方法
p6-6 (p94): 6.6一些真实时间序列的识别
p6-7 (p99): 6.7小结
p6-8 (p99): 习题
p7 (p105): 第7章 参数估计
p7-1 (p105): 7.1矩估计
p7-2 (p108): 7.2最小二乘估计
p7-3 (p112): 7.3极大似然与无条件最小二乘
p7-4 (p113): 7.4估计的性质
p7-5 (p115): 7.5参数估计例证
p7-6 (p118): 7.6自助法估计ARIMA模型
p7-7 (p120): 7.7小结
p7-8 (p120): 习题
p8 (p125): 第8章 模型诊断
p8-1 (p125): 8.1残差分析
p8-2 (p132): 8.2过度拟合和参数冗余
p8-3 (p134): 8.3小结
p8-4 (p135): 习题
p9 (p137): 第9章 预测
p9-1 (p137): 9.1最小均方误差预测
p9-2 (p137): 9.2确定性趋势
p9-3 (p138): 9.3 ARIMA预测
p9-4 (p145): 9.4预测极限
p9-5 (p146): 9.5预测的图示
p9-6 (p148): 9.6 ARIMA预测的更新
p9-7 (p148): 9.7预测的权重与指数加权滑动平均
p9-8 (p149): 9.8变换序列的预测
p9-9 (p151): 9.9某些ARIMA模型预测的总结
p9-10 (p152): 9.10小结
p9-11 (p152): 习题
p9-12 (p156): 附录E条件期望
p9-13 (p157): 附录F最小均方误差预测
p9-14 (p158): 附录G截断线性过程
p9-15 (p160): 附录H状态空间模型
p10 (p164): 第10章 季节模型
p10-1 (p165): 10.1季节ARIMA模型
p10-2 (p166): 10.2乘法季节ARMA模型
p10-3 (p168): 10.3非平稳季节ARIMA模型
p10-4 (p169): 10.4模型识别、拟合和检验
p10-5 (p174): 10.5季节模型预测
p10-6 (p178): 10.6小结
p10-7 (p178): 习题
p11 (p180): 第11章 时间序列回归模型
p11-1 (p180): 11.1干预分析
p11-2 (p185): 11.2异常值
p11-3 (p188): 11.3伪相关
p11-4 (p191): 11.4预白化与随机回归
p11-5 (p198): 11.5小结
p11-6 (p198): 习题
p12 (p201): 第12章 异方差时间序列模型
p12-1 (p201): 12.1金融时间序列的一些共同特征
p12-2 (p206): 12.2 ARCH（1）模型
p12-3 (p209): 12.3 GARCH模型
p12-4 (p214): 12.4极大似然估计
p12-5 (p217): 12.5模型诊断
p12-6 (p221): 12.6条件方差非负条件
p12-7 (p223): 12.7 GARCH模型的一些扩展
p12-8 (p224): 12.8另一个示例：USD/HKD汇率日数据
p12-9 (p226): 12.9小结
p12-10 (p226): 习题
p12-11 (p228): 附录Ⅰ广义混合检验公式
p13 (p229): 第13章 谱分析入门
p13-1 (p229): 13.1引言
p13-2 (p231): 13.2周期图
p13-3 (p235): 13.3谱表示和谱分布
p13-4 (p237): 13.4谱密度
p13-5 (p238): 13.5 ARMA过程的谱密度
p13-6 (p243): 13.6样本谱密度的抽样性质
p13-7 (p247): 13.7小结
p13-8 (p247): 习题
p13-9 (p250): 附录J余弦与正弦序列的正交性
p14 (p251): 第14章 谱估计
p14-1 (p251): 14.1平滑谱密度
p14-2 (p253): 14.2偏差和方差
p14-3 (p254): 14.3带宽
p14-4 (p254): 14.4谱置信区间
p14-5 (p256): 14.5泄露和锥削
p14-6 (p259): 14.6自回归谱估计
p14-7 (p259): 14.7模拟数据示例
p14-8 (p264): 14.8真实数据示例
p14-9 (p268): 14.9其他谱估计法
p14-10 (p269): 14.10小结
p14-11 (p269): 习题
p14-12 (p271): 附录K锥削与狄利克雷核
p15 (p273): 第15章 门限模型
p15-1 (p274): 15.1用图解法探索非线性
p15-2 (p278): 15.2非线性检验
p15-3 (p280): 15.3多项式模型一般是爆炸性的
p15-4 (p282): 15.4一阶门限自回归模型
p15-5 (p285): 15.5门限模型
p15-6 (p285): 15.6门限非线性的检验
p15-7 (p287): 15.7 TAR模型的估计
p15-8 (p293): 15.8模型诊断
p15-9 (p295): 15.9预测
p15-10 (p298): 15.10小结
p15-11 (p298): 习题
p15-12 (p299): 附录L TAR广义混合检验
p16 (p301): 附录Ⅰ R入门
p17 (p339): 附录Ⅱ数据集合的说明
p18 (p342): 参考文献

"Time Series Analysis With Applications in R, Second Edition, presents an accessible approach to understanding time series models and their applications. Although the emphasis is on time domain ARIMA models and their analysis, the new edition devotes two chapters to the frequency domain and three to time series regression models, models for heteroscedasticty, and threshold models. All of the ideas and methods are illustrated with both real and simulated data sets." "A unique feature of this edition is its integration with the R computing environment. The tables and graphical displays are accompanied by the R commands used to produce them. An extensive R package, TSA, which contains many new or revised R functions and all of the data used in the book, accompanies the written text. Script files of R commands for each chapter are available for download. There is also an extensive appendix in the book that leads the reader through the use of R commands and the new R package to carry out the analyses."--Jacket

This book has been developed for a one-semester course usually attended by students in statistics, economics, business, engineering, and quantitative social sciences. A unique feature of this edition is its integration with the R computing environment. Basic applied statistics is assumed through multiple regression. Calculus is assumed only to the extent of minimizing sums of squares but a calculus-based introduction to statistics is necessary for a thorough understanding of some of the theory. Actual time series data drawn from various disciplines are used throughout the book to illustrate the methodology.

Ben shu yi yi yu li jie de fang shi jiang shu le shi jian xu lie mo xing ji qi ying yong,Zhu yao nei rong bao kuo:qu shi,Ping wen shi jian xu lie mo xing,Fei ping wen shi jian xu lie mo xing,Mo xing shi bie,Can shu gu ji,Mo xing zhen duan,Yu ce,Ji jie mo xing,Shi jian xu lie hui gui mo xing,Yi fang cha shi jian xu lie mo xing,Pu fen xi ru men,Pu gu ji,Men xian mo xing.Dui suo you de si xiang he fang fa,Dou yong zhen shi shu ju ji he mo ni shu ju ji jin xing le shuo ming.

Introduction
Fundamental concepts
Trends
Models for stationary time series
Models for nonstationary time series
Model specification
Parameter estimation
Model diagnostics
Forecasting
Seasonal models
Time series regression models
Time series models of heteroscedasticity
Introduction to spectral analysis
Estimating the spectrum
Threshold models
Appendix: an introduction to R.

本书以易于理解的方式讲述了时间序列模型及其应用, 主要内容包括:趋势, 平稳时间序列模型, 非平稳时间序列模型, 模型识别, 参数估计, 模型诊断, 预测, 季节模型, 时间序列回归模型, 异方差时间序列模型, 谱分析入门, 谱估计, 门限模型.对所有的思想和方法, 都用真实数据集和模拟数据集进行了说明

Springer Texts in Statistics
Erscheinungsdatum: 04.04.2008

2023-12-04