Direction Dependence in Statistical Modeling Methods of Analysis.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Wiedermann, Wolfgang
Otros Autores: Kim, Daeyoung, 1975-, Sungur, Engin A., von Eye, Alexander
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2020.
Temas:
Dependence (Statistics)
Dépendance (Statistique)
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • Contents
  • About the Editors
  • Notes on Contributors
  • Acknowledgments
  • Preface
  • Part I Fundamental Concepts of Direction Dependence
  • Chapter 1 From Correlation to Direction Dependence Analysis 1888-2018
  • 1.1 Introduction
  • 1.2 Correlation as a Symmetrical Concept of X and Y
  • 1.3 Correlation as an Asymmetrical Concept of X and Y
  • 1.4 Outlook and Conclusions
  • References
  • Chapter 2 Direction Dependence Analysis: Statistical Foundations and Applications
  • 2.1 Some Origins of Direction Dependence Research
  • 2.2 Causation and Asymmetry of Dependence
  • 2.3 Foundations of Direction Dependence
  • 2.3.1 Data Requirements
  • 2.3.2 DDA Component I: Distributional Properties of Observed Variables
  • 2.3.3 DDA Component II: Distributional Properties of Errors
  • 2.3.4 DDA Component III: Independence Properties
  • 2.3.5 Presence of Confounding
  • 2.3.6 An Integrated Framework
  • 2.4 Direction Dependence in Mediation
  • 2.5 Direction Dependence in Moderation
  • 2.6 Some Applications and Software Implementations
  • 2.7 Conclusions and Future Directions
  • References
  • Chapter 3 The Use of Copulas for Directional Dependence Modeling
  • 3.1 Introduction and Definitions
  • 3.1.1 Why Copulas?
  • 3.1.2 Defining Directional Dependence
  • 3.2 Directional Dependence Between Two Numerical Variables
  • 3.2.1 Asymmetric Copulas
  • 3.2.2 Regression Setting
  • 3.2.3 An Alternative Approach to Directional Dependence
  • 3.3 Directional Association Between Two Categorical Variables
  • 3.4 Concluding Remarks and Future Directions
  • References
  • Part II Direction Dependence in Continuous Variables
  • Chapter 4 Asymmetry Properties of the Partial Correlation Coefficient: Foundations for Covariate Adjustment in Distribution-Based Direction Dependence Analysis
  • 4.1 Asymmetry Properties of the Partial Correlation Coefficient
  • 4.2 Direction Dependence Measures when Errors Are Non-Normal
  • 4.3 Statistical Inference on Direction Dependence
  • 4.4 Monte-Carlo Simulations
  • 4.4.1 Study I: Parameter Recovery
  • 4.4.1.1 Results
  • 4.4.2 Study II: CI Coverage and Statistical Power
  • 4.4.2.1 Type I Error Coverage
  • 4.4.2.2 Statistical Power
  • 4.5 Data Example
  • 4.6 Discussion
  • 4.6.1 Relation to Causal Inference Methods
  • References
  • Chapter 5 Recent Advances in Semi-Parametric Methods for Causal Discovery
  • 5.1 Introduction
  • 5.2 Linear Non-Gaussian Methods
  • 5.2.1 LiNGAM
  • 5.2.2 Hidden Common Causes
  • 5.2.3 Time Series
  • 5.2.4 Multiple Data Sets
  • 5.2.5 Other Methodological Issues
  • 5.3 Nonlinear Bivariate Methods
  • 5.3.1 Additive Noise Models
  • 5.3.1.1 Post-Nonlinear Models
  • 5.3.1.2 Discrete Additive Noise Models
  • 5.3.2 Independence of Mechanism and Input
  • 5.3.2.1 Information-Geometric Approach for Causal Inference

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