时间序列与预测（英文版）（第2版）

图书标准信息

• 作者 Peter、Richard A.Davis 著
• 出版社 人民邮电出版社
• 出版时间 2009-03
• 版次 1
• ISBN 9787115196828
• 定价 69.00元
• 装帧 平装
• 开本 16开
• 纸张 其他
• 页数 437页
• 正文语种 英语
• 丛书 图灵数学统计学丛书

【内容简介】

　　《时间序列与预测(英文版)(第2版)》是时间序列领域的名著。特色在于注重实际应用。深浅适中，适用面广，示例和习题丰富，有微积分、线性代数和统计学基础知识即可阅读。书中全面介绍了经济、工程、自然科学和社会科学中所用的时间序列和预测方法，核心内容是平稳过程、ARMA模型和ARIMA模型、多元时间序列和状态空间模型、谱分析。书中配有时间序列软件包ITSM2000学生版，更加方便读者学习。

【作者简介】

　　PeterJ．Brockwell世界著名统计学家。ASA（美国统计协会）、IMS（数理统计学会）会士。科罗拉多州立大学统计系荣休教授。他是JournalofTimeSeriesAnalysis副主编，并LiRichardA．Davis合作开发了时间序列软件包ITSM2000。
　　RichardA．Davis世界著名统计学家。ASA（美国统计协会）、IMS（数理统计学会）会士。科罗拉多州立大学统计系教授，1997年至2005年担任该系的系主任。1998年荣获计量经济学Koopmans奖。他是StochasticProcessesandTheirApplications，AnnalsofAppliedProbability等期刊编委，是ProceedingsoftheAmericanMathematicsSociety的统计学领域主编。

【目录】

1.Introduction
1.1.ExamplesofTimeSeries
1.2.ObjectivesofTimeSeriesAnalysis
1.3.SomeSimpleTimeSeriesModels
1.3.1.SomeZero-MeanModels
1.3.2.ModelswithTrendandSeasonality
1.3.3.AGeneralApproachtoTimeSeriesModeling
1.4.StationaryModelsandtheAutocorrelationFunction
1.4.1.TheSampleAutocorrelationFunction
1.4.2.AModelfortheLakeHuronData
1.5.EstimationandEliminationofTrendandSeasonalComponents
1.5.1.EstimationandEliminationofTrendintheAbsenceof
Seasonality
1.5.2.EstimationandEliminationofBothTrendand
Seasonality
1.6.TestingtheEstimatedNoiseSequence
Problems

2.StationaryProcesses
2.1.BasicProperties
2.2.LinearProcesses
2.3.IntroductiontoARMAProcesses
2.4.PropertiesoftheSampleMeanandAutocorrelationFunction
2.4.1.Estimationoftz
2.4.2.Estimationofy(.)andp(.)
2.5.ForecastingStationaryTimeSeries
2.5.1.TheDurbin-LevinsonAlgorithm
2.5.2.TheInnovationsAlgorithm
2.5.3.PredictionofaStationaryProcessinTermsofInfinitely
ManyPastValues
2.6.TheWoldDecomposition
Problems

3.ARMAModels
3.1.ARMA(p，q)Processes
3.2.TheACFandPACFofanARMA(p，q)Process
3.2.1.CalculationoftheACVF
3.2.2.TheAutocorrelationFunction
3.2.3.ThePartialAutocorrelationFunction
3.2.4.Examples
3.3.ForecastingARMAProcesses
Problems

4.SpectralAnalysis
4.1.SpectralDensities
4.2.ThePeriodogram
4.3.Time-InvariantLinearFilters
4.4.TheSpectralDensityofanARMAProcess
Problems

5.ModelingandForecastingwithARMAProcesses
5.I.PreliminaryEstimation
5.1.1.Yule-WalkerEstimation
5.1.2.BurgsAlgorithm
5.1.3.TheInnovationsAlgorithm
5.1.4.TheHannan-RissanenAlgorithm
5.2.MaximumLikelihoodEstimation
5.3.DiagnosticChecking
5.3.1.TheGraphof
5.3.2.TheSampleACFoftheResiduals
5.3.3.TestsforRandomnessoftheResiduals
5.4.Forecasting
5.5.OrderSelection
5.5.1.TheFPECriterion
5.5.2.TheAICCCriterion
Problems

6.NonstationaryandSeasonalTimeSeriesModels
6.1.ARIMAModelsforNonstationaryTimeSeries
6.2.IdentificationTechniques
6.3.UnitRootsinTimeSeriesModels
6.3.1.UnitRootsinAutoregressions
6.3.2.UnitRootsinMovingAverages
6.4.ForecastingARIMAModels
6.4.1.TheForecastFunction
6.5.SeasonalARIMAModels
6.5.1.ForecastingSARIMAProcesses
6.6.RegressionwithARMAErrors
6.6.1.OLSandGLSEstimation
6.6.2.MLEstimation
Problems

7.MultivariateTimeSeries
7.1.Examples
7.2.Second-OrderPropertiesofMultivariateTimeSeries
7.3.EstimationoftheMeanandCovarianceFunction
7.3.1.Estimationof
7.3.2.EstimationofF(h)
7.3.3.TestingforIndependenceofTwoStationaryTimeSeries
7.3.4.BartlettsFormula
7.4.MultivariateARMAProcesses
7.4.1.TheCovarianceMatrixFunctionofaCausalARMA
Process
7.5.BestLinearPredictorsofSecond-OrderRandomVectors
7.6.ModelingandForecastingwithMultivariateARProcesses
7.6.1.EstimationforAutoregressiveProcessesUsingWhittles
Algorithm
7.6.2.ForecastingMultivariateAutoregressiveProcesses
7.7.Cointegration
Problems

8.State-SpaceModels
8.1.State-SpaceRepresentations
8.2.TheBasicStructuralModel
8.3.State-SpaceRepresentationofARIMAModels
8.4.TheKalmanRecursions
8.5.EstimationForState-SpaceModels
8.6.State-SpaceModelswithMissingObservations
8.7.TheEMAlgorithm
8.8.GeneralizedState-SpaceModels
8.8.1.Parameter-DrivenModels
8.8.2.Observation-DrivenModels
Problems

9.ForecastingTechniques
9.1.TheARARAlgorithm
9.1.1.MemoryShortening
9.1.2.FittingaSubsetAutoregression
9.1.3.Forecasting
9.1.4.ApplicationoftheARARAlgorithm
9.2.TheHolt-WintersAlgorithm
9.2.1.TheAlgorithm
9.2.2.Holt-WintersandARIMAForecasting
9.3.TheHolt-WintersSeasonalAlgorithm
9.3.1.TheAlgorithm
9.3.2.Holt-WintersSeasonalandARIMAForecasting
9.4.ChoosingaForecastingAlgorithm
Problems

10.FurtherTopics
10.1.TransferFunctionModels
10.1.1.PredictionBasedonaTransferFunctionModel
10.2.InterventionAnalysis
10.3.NonlinearModels
10.3.1.DeviationsfromLinearity
10.3.2.ChaoticDeterministicSequences
10.3.3.DistinguishingBetweenWhiteNoiseandiidSequences
10.3.4.ThreeUsefulClassesofNonlinearModels
10.3.5.ModelingVolatility
10.4.Continuous-TimeModels
10.5.Long-MemoryModels
Problems

A.RandomVariablesandProbabilityDistributions
A.1.DistributionFunctionsandExpectation
A.2.RandomVectors
A.3.TheMultivariateNormalDistribution
Problems
BStatisticalComplements
CMeanSquareConvergence
DAnITSMTutorial
References
Index