| Description |
1 online resource (435 p.) |
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Notes |
Description based upon print version of record |
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5.3.2.2 Causal Inference with Unsupervised Inverse Regression |
| Subject |
Dependence (Statistics)
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Dependence (Statistics)
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| Form |
Electronic book
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| Author |
Kim, Daeyoung, 1975-
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Sungur, Engin A.
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von Eye, Alexander
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| ISBN |
9781119523130 |
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1119523133 |
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