Direction Dependence in Statistical Modeling (eBook, PDF)

Methods of Analysis
Redaktion: Wiedermann, Wolfgang; Eye, Alexander Von; Sungur, Engin A.; Kim, Daeyoung

• Format: PDF

Produktbeschreibung

Covers the latest developments in direction dependence research Direction Dependence in Statistical Modeling: Methods of Analysis incorporates the latest research for the statistical analysis of hypotheses that are compatible with the causal direction of dependence of variable relations. Having particular application in the fields of neuroscience, clinical psychology, developmental psychology, educational psychology, and epidemiology, direction dependence methods have attracted growing attention due to their potential to help decide which of two competing statistical models is more likely to reflect the correct causal flow. The book covers several topics in-depth, including: * A demonstration of the importance of methods for the analysis of direction dependence hypotheses * A presentation of the development of methods for direction dependence analysis together with recent novel, unpublished software implementations * A review of methods of direction dependence following the copula-based tradition of Sungur and Kim * A presentation of extensions of direction dependence methods to the domain of categorical data * An overview of algorithms for causal structure learning The book's fourteen chapters include a discussion of the use of custom dialogs and macros in SPSS to make direction dependence analysis accessible to empirical researchers.

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Produktdetails

• Verlag: For Dummies
• Seitenzahl: 432
• Erscheinungstermin: 9. November 2020
• Englisch
• ISBN-13: 9781119523130
• Artikelnr.: 60567999

Autorenporträt

WOLFGANG WIEDERMANN is Associate Professor at the University of Missouri-Columbia. He received his Ph.D. in Quantitative Psychology from the University of Klagenfurt, Austria. His primary research interests include the development of methods for causal inference, methods to determine the causal direction of dependence in observational data, and methods for person-oriented research settings. He has edited books on advances in statistical methods for causal inference (with von Eye, Wiley) and new developments in statistical methods for dependent data analysis in the social and behavioral sciences (with Stemmler and von Eye). DAEYOUNG KIM is Associate Professor of Mathematics and Statistics at the University of Massachusetts, Amherst. He received his Ph.D. from the Pennsylvania State University in Statistics. His original research interests were in likelihood inference in finite mixture modelling including empirical identifiability and multimodality, development of geometric and computational methods to delineate multidimensional inference functions, and likelihood inference in incompletely observed categorical data, followed by a focus on the analysis of asymmetric association in multivariate data using (sub)copula regression. ENGIN A. SUNGUR has a B.A. in City and Regional Planning (Middle East Technical University, METU, Turkey), M.S. in Applied Statistics, METU, M.S. in Statistics (Carnegie-Mellon University, CMU) and Ph.D. in Statistics (CMU). He taught at Carnegie-Mellon University, University of Pittsburg, Middle East Technical University, and University of Iowa. Currently, he is a Morse-Alumni distinguished professor of statistics at University of Minnesota Morris. He is teaching statistics for more than 38 years, 29 years of which is at the University of Minnesota Morris. His research areas are dependence modeling with emphasis on directional dependence, modern multivariate statistics, extreme value theory, and statistical education. ALEXANDER VON EYE is Professor Emeritus of Psychology at Michigan State University (MSU). He received his Ph.D. in Psychology from the University of Trier, Germany. He received his accreditation as Professional Statistician from the American Statistical Association (PSTATTM). His research focuses (1) on the development and testing of statistical methods for the analysis of categorical and longitudinal data, and for the analysis of direction dependence hypotheses. In addition (2), he is member of a research team at MSU (with Bogat, Levendosky, and Lonstein) that investigates the effects of violence on women and their newborn children. His third area of interest (3) concerns theoretical developments and applied analysis of person-orientation in empirical research.

Inhaltsangabe

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