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Linear models and time-series analysis : regression, ANOVA, ARMA and GARCH / Dr. Marc S. Paolella

By: Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Wiley series in probability and statisticsPublisher: Hoboken, NJ : John Wiley & Sons, 2018Description: 1 Online-RessourceISBN:
  • 9781119431985
  • 9781119431855
Subject(s): Additional physical formats: 9781119431909 | Erscheint auch als: Linear models and time-series analysis. Druck-Ausgabe Hoboken, NJ : Wiley, 2019. xvi, 880 SeitenDDC classification:
  • 515.5/5
MSC: MSC: *62-01 | 62M10 | 62J05 | 62J10 | 62P05RVK: RVK: ST 301LOC classification:
  • QA280
Online resources: Summary: 1.4.5 Testing With Nonzero h1.4.6 Examples; 1.4.7 Confidence Intervals; 1.5 Alternative Residual Calculation; 1.6 Further Topics; 1.7 Problems; 1.A Appendix: Derivation of the BLUS Residual Vector; 1.B Appendix: The Recursive Residuals; 1.C Appendix: Solutions; Chapter 2 Fixed Effects ANOVA Models; 2.1 Introduction: Fixed, Random, and Mixed Effects Models; 2.2 Two Sample t‐Tests for Differences in Means; 2.3 The Two Sample t‐Test with Ignored Block Effects; 2.4 One‐Way ANOVA with Fixed Effects; 2.4.1 The Model; 2.4.2 Estimation and Testing; 2.4.3 Determination of Sample SizeSummary: 2.4.4 The ANOVA Table2.4.5 Computing Confidence Intervals; 2.4.6 A Word on Model Assumptions; 2.5 Two‐Way Balanced Fixed Effects ANOVA; 2.5.1 The Model and Use of the Interaction Terms; 2.5.2 Sums of Squares Decomposition Without Interaction; 2.5.3 Sums of Squares Decomposition With Interaction; 2.5.4 Example and Codes; Chapter 3 Introduction to Random and Mixed Effects Models; 3.1 One-Factor Balanced Random Effects Model; 3.1.1 Model and Maximum Likelihood Estimation; 3.1.2 Distribution Theory and ANOVA Table; 3.1.3 Point Estimation, Interval Estimation, and Significance TestingSummary: 3.1.4 Satterthwaite's Method3.1.5 Use of SAS; 3.1.6 Approximate Inference in the Unbalanced Case; 3.1.6.1 Point Estimation in the Unbalanced Case; 3.1.6.2 Interval Estimation in the Unbalanced Case; 3.2 Crossed Random Effects Models; 3.2.1 Two Factors; 3.3 Nested Random Effects Models; 3.3.1 Two Factors; 3.3.1.3 Mixed Model Case; 3.3.2 Three Factors; 3.3.2.1 All Effects Random; 3.3.2.2 Mixed: Classes Fixed; 3.3.2.3 Mixed: Classes and Subclasses Fixed; 3.4 Problems; 3.A Appendix: Solutions; Part II Time Series Analysis: ARMAX Processes; Chapter 4 The AR(1) Model; 4.1 Moments and StationaritySummary: 4.2 Order of Integration and Long‐Run Variance4.3 Least Squares and ML Estimation; 4.3.1 OLS Estimator of a; 4.3.2 Likelihood Derivation I; 4.3.3 Likelihood Derivation II; 4.3.4 Likelihood Derivation III; 4.3.5 Asymptotic Distribution; 4.4 Forecasting; 4.5 Small Sample Distribution of the OLS and ML Point Estimators; 4.6 Alternative Point Estimators of a; 4.6.1 Use of the Jackknife for Bias Reduction; 4.6.2 Use of the Bootstrap for Bias Reduction; 4.6.3 Median‐Unbiased Estimator; 4.6.4 Mean‐Bias Adjusted Estimator; 4.6.5 Mode‐Adjusted Estimator; 4.6.6 ComparisonSummary: Cover; Title Page; Copyright; Contents; Preface; Part I Linear Models: Regression and ANOVA; Chapter 1 The Linear Model; 1.1 Regression, Correlation, and Causality; 1.2 Ordinary and Generalized Least Squares; 1.2.1 Ordinary Least Squares Estimation; 1.2.2 Further Aspects of Regression and OLS; 1.2.3 Generalized Least Squares; 1.3 The Geometric Approach to Least Squares; 1.3.1 Projection; 1.3.2 Implementation; 1.4 Linear Parameter Restrictions; 1.4.1 Formulation and Estimation; 1.4.2 Estimability and Identifiability; 1.4.3 Moments and the Restricted GLS Estimator; 1.4.4 Testing With h=0PPN: PPN: 1040002854Package identifier: Produktsigel: ZDB-4-NLEBK
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