线性模型 C.R.Rao,H.Toutenburg 著世界图书出版公司出版社
正版库存一手书，品相视出版时间长短而定，自然成色，可开电子发票，图片由软件自动采集，以书名为准，不以图片不符售后！
￥ 43.1 7.7折 ￥ 56 全新
库存20件
作者C.R.Rao,H.Toutenburg 著
出版社世界图书出版公司出版社
ISBN9787506238182
出版时间1998-08
版次1
装帧平装
开本16开
纸张胶版纸
页数352页
定价56元
货号R_2065186
上书时间2023-12-15
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- 正版库存一手书，品相视出版时间长短而定，自然成色，可开电子发票，图片由软件自动采集，以书名为准，不以图片不符售后！
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基本信息
书名:线性模型
定价：56元
作者:C.R.Rao,H.Toutenburg 著
出版社：世界图书出版公司出版社
出版日期：1998-08-01
ISBN：9787506238182
字数：
页码：352
版次：
装帧：平装
开本：32开
商品重量：
编辑
The book iased on both authors' several years of experience in teaching linear models at various levels. It gives an up-to-date account of the theory and applications of linear models. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for courses in other areas. Some of the highlights in thiook are as follows. 本书为英文版。
内容提要
The book iased on both authors' several years of experience in teaching linear models at various levels. It gives an up-to-date account of the theory and applications of linear models. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for courses in other areas. Some of the highlights in thiook are as follows.
目录
Preface1 Introduction2 LinearModels 2.1 RegressionModelsinEconometrics 2.2 EconometricModels 2.3 TheReducedForm 2.4 TheMultivariateRegressionModel 2.5 TheClassicalMultivariateLinearRegressionModel 2.6 TheGeneralizedLinearRegressionModel3 TheLinearRegressionModel 3.1 TheLinearModel 3.2 ThePrincipleofOrdinaryLeastSquares(OLS) 3.3 GeometricPropertiesofOLS 3.4 BestLinearUnbiasedEstimation 3.5 Estimation(Prediction)oftheErrorTermeand2 3.6 ClassicalRegressionunderNormalErrors 3.7 TestingLinearHypotheses 3.8 AnalysisofVarianceandGoodnessofFit 3.9 TheCanonicalForm 3.10 MethodsforDealingwithMulticollinearity 3.11 ProjectionPursuitRegression 3.12 TotalLeastSquares 3.13 MinimaxEstimation 3.14 CensoredRegression4 TheGeneralizedLinearRegressionModel 4.1 OptimalLinearEstimationofB 4.2 TheAitkenEstimator 4.3 MisspecificationoftheDispersionMatrix 4.4 HeteroscedasticityandAutoregression5 ExactandStochasticLinearRestrictions 5.1 UseofPriorInformation 5.2 TheRestrictedLeast-SquaresEstimator 5.3 StepwiseInclusionofExactLinearRestrictions 5.4 BiasedLinearRestrictionsandMDEComparisonwiththeOLSE 5.5 MDEMatrixComparisonsofTwoBiasedEstimators 5.6 MDEMatrixComparisonofTwoLinearBiasedEstimators 5.7 MDEComparisonofTwo(Biased)RestrictedEstimators 5.8 StochasticLinearRestrictions 5.9 WeakenedLinearRestrictions6 PredictionProblemsintheGeneralizedRegressionModel 6.1 Introduction 6.2 SomeSimpleLinearModels 6.3 ThePredictionModel 6.4 OptimalHeterogeneousPrediction 6.5 OptimalHomogeneousPrediction 6.6 MDEMatrixComparisonsbetweenOptimalandClassical Predictors 6.7 PredictionRegions7 SensitivityAnalysis 7.1 Introduction 7.2 PredictionMatrix 7.3 TheEffectofaSingleObservationontheEstimationofPa-rameters 7.4 DiagnosticPlotsforTestingtheModelAssumptions 7.5 MeasuresBasedontheConfidenceEllipsoid 7.6 PartialRegressionPlots8 AnalysisofIncompleteDataSets 8.1 StatisticalAnalysiswithMissingData 8.2 MissingDataintheResponse 8.3 MissingValuesintheX-Matrix 8.4 MaximumLikelihoodEstimatesofMissingValues 8.5 WeightedMixedRegression9 RobustRegression 9.1 Introduction 9.2 LeastAbsoluteDeviationEstimators--UnivariateCase ……10 ModelsforBinaryResponseVariablesA MatrixAlgebraB TablesReferencesIndex
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