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Produktbild: Statistics and Causality

Statistics and Causality Methods for Applied Empirical Research

Aus der Reihe Wiley Series in Probability and Statistics

145,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

07.06.2016

Herausgeber

Wolfgang Wiedermann + weitere

Verlag

John Wiley & Sons Inc

Seitenzahl

480

Maße (L/B/H)

23,6/15,5/3 cm

Gewicht

794 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-118-94704-3

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

07.06.2016

Herausgeber

Verlag

John Wiley & Sons Inc

Seitenzahl

480

Maße (L/B/H)

23,6/15,5/3 cm

Gewicht

794 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-118-94704-3

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

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  • Produktbild: Statistics and Causality
  • LIST OF CONTRIBUTORS xiii
     
    PREFACE xvii
     
    ACKNOWLEDGMENTS xxv
     
    PART I BASES OF CAUSALITY 1
     
    1 Causation and the Aims of Inquiry 3
    Ned Hall
     
    1.1 Introduction, 3
     
    1.2 The Aim of an Account of Causation, 4
     
    1.3 The Good News, 7
     
    1.4 The Challenging News, 17
     
    1.5 The Perplexing News, 26
     
    2 Evidence and Epistemic Causality 31
    Michael Wilde and Jon Williamson
     
    2.1 Causality and Evidence, 31
     
    2.2 The Epistemic Theory of Causality, 35
     
    2.3 The Nature of Evidence, 38
     
    2.4 Conclusion, 40
     
    PART II DIRECTIONALITY OF EFFECTS 43
     
    3 Statistical Inference for Direction of Dependence in Linear Models 45
    Yadolah Dodge and Valentin Rousson
     
    3.1 Introduction, 45
     
    3.2 Choosing the Direction of a Regression Line, 46
     
    3.3 Significance Testing for the Direction of a Regression Line, 48
     
    3.4 Lurking Variables and Causality, 54
     
    3.5 Brain and Body Data Revisited, 57
     
    3.6 Conclusions, 60
     
    4 Directionality of Effects in Causal Mediation Analysis 63
    Wolfgang Wiedermann and Alexander von Eye
     
    4.1 Introduction, 63
     
    4.2 Elements of Causal Mediation Analysis, 66
     
    4.3 Directionality of Effects in Mediation Models, 68
     
    4.4 Testing Directionality Using Independence Properties of Competing Mediation Models, 71
     
    4.5 Simulating the Performance of Directionality Tests, 82
     
    4.6 Empirical Data Example: Development of Numerical Cognition, 85
     
    4.7 Discussion, 92
     
    5 Direction of Effects in Categorical Variables: A Structural Perspective 107
    Alexander von Eye and Wolfgang Wiedermann
     
    5.1 Introduction, 107
     
    5.2 Concepts of Independence in Categorical Data Analysis, 108
     
    5.3 Direction Dependence in Bivariate Settings: Metric and Categorical Variables, 110
     
    5.4 Explaining the Structure of Cross-Classifications, 117
     
    5.5 Data Example, 123
     
    5.6 Discussion, 126
     
    6 Directional Dependence Analysis Using Skew-Normal Copula-Based Regression 131
    Seongyong Kim and Daeyoung Kim
     
    6.1 Introduction, 131
     
    6.2 Copula-Based Regression, 133
     
    6.3 Directional Dependence in the Copula-Based Regression, 136
     
    6.4 Skew-Normal Copula, 138
     
    6.5 Inference of Directional Dependence Using Skew-Normal Copula-Based Regression, 144
     
    6.6 Application, 147
     
    6.7 Conclusion, 150
     
    7 Non-Gaussian Structural Equation Models for Causal Discovery 153
    Shohei Shimizu
     
    7.1 Introduction, 153
     
    7.2 Independent Component Analysis, 156
     
    7.3 Basic Linear Non-Gaussian Acyclic Model, 158
     
    7.4 LINGAM for Time Series, 167
     
    7.5 LINGAM with Latent Common Causes, 169
     
    7.6 Conclusion and Future Directions, 177
     
    8 Nonlinear Functional Causal Models for Distinguishing Cause from Effect 185
    Kun Zhang and Aapo Hyvärinen
     
    8.1 Introduction, 185
     
    8.2 Nonlinear Additive Noise Model, 188
     
    8.3 Post-Nonlinear Causal Model, 192
     
    8.4 On the Relationships Between Different Principles for Model Estimation, 194
     
    8.5 Remark on General Nonlinear Causal Models, 196
     
    8.6 Some Empirical Results, 197
     
    8.7 Discussion and Conclusion, 198
     
    PARTIII GRANGER CAUSALITY AND LONGITUDINAL DATA MODELING 203
     
    9 Alternative Forms of Granger Causality, Heterogeneity, and Nonstationarity 205
    Peter C. M. Molenaar and Lawrence L. Lo
     
    9.1 Introduction, 205
     
    9.2 Some Initial Remarks on the Logic of Granger Causality Testing, 206
     
    9.3 Preliminary Introduction to Time Series Analysis