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Produktbild: Direction Dependence in Statistical Modeling

Direction Dependence in Statistical Modeling Methods of Analysis

147,99 €

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

03.12.2020

Herausgeber

Wolfgang Wiedermann + weitere

Verlag

John Wiley & Sons Inc

Seitenzahl

432

Maße (L/B/H)

21,8/15,2/2,5 cm

Gewicht

771 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-52307-9

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

03.12.2020

Herausgeber

Verlag

John Wiley & Sons Inc

Seitenzahl

432

Maße (L/B/H)

21,8/15,2/2,5 cm

Gewicht

771 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-52307-9

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

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  • Produktbild: Direction Dependence in Statistical Modeling
  • About the Editors xv
     
    Notes on Contributors xvii
     
    Acknowledgments xxi
     
    Preface xxiii
     
    Part I Fundamental Concepts of Direction Dependence 1
     
    1 From Correlation to Direction Dependence Analysis 1888-2018 3
    Yadolah Dodge and Valentin Rousson
     
    1.1 Introduction 3
     
    1.2 Correlation as a Symmetrical Concept of X and Y 4
     
    1.3 Correlation as an Asymmetrical Concept of X and Y 5
     
    1.4 Outlook and Conclusions 6
     
    References 6
     
    2 Direction Dependence Analysis: Statistical Foundations and Applications 9
    Wolfgang Wiedermann, Xintong Li, and Alexander von Eye
     
    2.1 Some Origins of Direction Dependence Research 11
     
    2.2 Causation and Asymmetry of Dependence 13
     
    2.3 Foundations of Direction Dependence 14
     
    2.3.1 Data Requirements 15
     
    2.3.2 DDA Component I: Distributional Properties of Observed Variables 16
     
    2.3.3 DDA Component II: Distributional Properties of Errors 19
     
    2.3.4 DDA Component III: Independence Properties 20
     
    2.3.5 Presence of Confounding 21
     
    2.3.6 An Integrated Framework 24
     
    2.4 Direction Dependence in Mediation 29
     
    2.5 Direction Dependence in Moderation 32
     
    2.6 Some Applications and Software Implementations 34
     
    2.7 Conclusions and Future Directions 36
     
    References 38
     
    3 The Use of Copulas for Directional Dependence Modeling 47
    Engin A. Sungur
     
    3.1 Introduction and Definitions 47
     
    3.1.1 Why Copulas? 48
     
    3.1.2 Defining Directional Dependence 48
     
    3.2 Directional Dependence Between Two Numerical Variables 51
     
    3.2.1 Asymmetric Copulas 52
     
    3.2.2 Regression Setting 59
     
    3.2.3 An Alternative Approach to Directional Dependence 62
     
    3.3 Directional Association Between Two Categorical Variables 70
     
    3.4 Concluding Remarks and Future Directions 74
     
    References 75
     
    Part II Direction Dependence in Continuous Variables 79
     
    4 Asymmetry Properties of the Partial Correlation Coefficient: Foundations for Covariate Adjustment in Distribution-Based Direction Dependence Analysis 81
    Wolfgang Wiedermann
     
    4.1 Asymmetry Properties of the Partial Correlation Coefficient 84
     
    4.2 Direction Dependence Measures when Errors Are Non-Normal 86
     
    4.3 Statistical Inference on Direction Dependence 89
     
    4.4 Monte-Carlo Simulations 90
     
    4.4.1 Study I: Parameter Recovery 90
     
    4.4.1.1 Results 91
     
    4.4.2 Study II: CI Coverage and Statistical Power 91
     
    4.4.2.1 Type I Error Coverage 94
     
    4.4.2.2 Statistical Power 94
     
    4.5 Data Example 98
     
    4.6 Discussion 101
     
    4.6.1 Relation to Causal Inference Methods 103
     
    References 105
     
    5 Recent Advances in Semi-Parametric Methods for Causal Discovery 111
    Shohei Shimizu and Patrick Blöbaum
     
    5.1 Introduction 111
     
    5.2 Linear Non-Gaussian Methods 113
     
    5.2.1 LiNGAM 113
     
    5.2.2 Hidden Common Causes 115
     
    5.2.3 Time Series 118
     
    5.2.4 Multiple Data Sets 119
     
    5.2.5 Other Methodological Issues 119
     
    5.3 Nonlinear Bivariate Methods 119
     
    5.3.1 Additive Noise Models 120
     
    5.3.1.1 Post-Nonlinear Models 121
     
    5.3.1.2 Discrete Additive Noise Models 121
     
    5.3.2 Independence of Mechanism and Input 121
     
    5.3.2.1 Information-Geometric Approach for Causal Inference 122
     
    5.3.2.2 Causal Inference with Unsupervised Inverse Regression 123
     
    5.3.2.3 Approximation of Kolmogorov Complexities via the Minimum Description Length Principle 123
     
    5.3.2.4 Regression Error Based Ca