时间序列分析及其应用第2版

图书标准信息

• 作者 [美]罗伯特沙姆韦 著
• 出版社 世界图书出版公司
• 出版时间 2009-05
• 版次 1
• ISBN 9787510004384
• 定价 69.00元
• 装帧 平装
• 开本 大32开
• 纸张 胶版纸
• 页数 575页
• 正文语种 英语

【内容简介】

　　Thegoalsofthisbookaretodevelopanappreciationfortherichnessandversatilityofmoderntimeseriesanalysisasatoolforanalyzingdata，andstillmaintainacommitmenttotheoreticalintegrity，asexemplifiedbytheseminalworksofBrillinger（1981）andHannan（1970）andthetextsbyBrockwellandDavis（1991）andFuller（1995）.Theadventofmorepowerfulcomputing，es-peciallyinthelastthreeyears，hasprovidedbothrealdataandnewsoftwarethatcantakeoneconsiderablybeyondthefittingofsimpletimedomainmod-els，suchashavebeenelegantlydescribedinthelandmarkworkofBoxandJenkins（seeBoxetal.，1994）.Thisbookisdesignedtobeusefulasatextforcoursesintimeseriesonseveraldifferentlevelsandasareferenceworkforpractitionersfacingtheanalysisoftime-correlateddatainthephysical，biological，andsocialsciences.

【目录】

1CharacteristicsofTimeSeries
1.1Introduction
1.2TheNatureofTimeSeriesData
1.3TimeSeriesStatisticalModels
1.4MeasuresofDependence:AutocorrelationandCross-Correlation
1.5StationaryTimeSeries
1.6EstimationofCorrelation
1.7Vector-ValuedandMultidimensionalSeries
Problems
2TimeSeriesRegressionandExploratoryDataAnalysis
2.1Introduction
2.2ClassicalRegressionintheTimeSeriesContext
2.3ExploratoryDataAnalysis
2.4SmoothingintheTimeSeriesContext
Problems
3ARIMAModels
3.1Introduction
3.2AutoregressiveMovingAverageModels
3.3DifferenceEquations
3.4AutocorrelationandPartialAutocorrelationFunctions
3.5Forecasting
3.6Estimation
3.7IntegratedModelsforNonstationaryData
3.8BuildingARIMAModels
3.9MultiplicativeSeasonalARIMAModels
Problems
4SpectralAnalysisandFiltering
4.1Introduction
4.2CyclicalBehaviorandPeriodicity
4.3TheSpectralDensity
4.4PeriodogramandDiscreteFourierTransform
4.5NonparametricSpectralEstimation
4.6MultipleSeriesandCross-Spectra
4.7LinearFilters
4.8ParametricSpectralEstimation
4.9DynamicFourierAnalysisandWavelets
4.10LaggedRegressionModels
4.11SignalExtractionandOptimumFiltering
4.12SpectralAnalysisofMultidimensionalSeries
Problems
5AdditionalTimeDomainTopics
5.1Introduction
5.2LongMemoryARMAandFractionalDifferencing
5.3GARCHModels
5.4ThresholdModels
5.5RegressionwithAutocorrelatedErrors
5.6LaggedRegression:TransferFunctionModeling
5.7MultivariateARMAXModels
Problems
6State-SpaceModels
6.1Introduction
6.2Filtering,Smoothing,andForecasting
6.3MaximumLikelihoodEstimation
6.4MissingDataModifications
6.5StructuralModels:SignalExtractionandForecasting
6.6ARMAXModelsinState-SpaceForm
6.7BootstrappingState-SpaceModels
6.8DynamicLinearModelswithSwitching
6.9NonlinearandNon-normalState-SpaceModelsUsingMonteCarloMethods
6.10StochasticVolatility
6.11State-SpaceandARMAXModelsforLongitudinalDataAnalysis
Problems
7StatisticalMethodsintheFrequencyDomain
7.1Introduction
7.2SpectralMatricesandLikelihoodFunctions
7.3RegressionforJointlyStationarySeries
7.4RegressionwithDeterministicInputs
7.5RandomCoefficientRegression
7.6AnalysisofDesignedExperiments
7.7DiscriminationandClusterAnalysis
7.8PrincipalComponentsandFactorAnalysis
7.9TheSpectralEnvelope
Problems
AppendixA:LargeSampleTheory
A.1ConvergenceModes
A.2CentralLimitTheorems
A.3TheMeanandAutocorrelationFunctions
AppendixB:TimeDomainTheory
B.1HilbertSpacesandtheProjectionTheorem
B.2CausalConditionsforARMAModels
B.3LargeSampleDistributionoftheAR(p)ConditionalLeastSquaresEstimators
B.4TheWoldDecomposition
AppendixC:SpectralDomainTheory
C.1SpectralRepresentationTheorem
C.2LargeSampleDistributionoftheDFTandSmoothedPeriodogram
C.3TheComplexMultivariateNormalDistribution
References
Index