Książki

This edited book examines current methods for the statistical analysis of hypotheses that are compatible with direction dependence. The proposed book is divided in four parts, each consisting of two or more chapters, for a total of 14 chapters. The first part of this book introduces the fundamental concepts of direction dependence in statistical models. The authors provide a historical view on the origins of studying the direction of dependence in a regression line. Various classes of copulas with directional dependence properties are introduced. In addition, an introduction into copula regression functions and concomitants of order statistics in directional dependence modeling is given. Part II of the proposed book is devoted to recent developments and advances in direction dependence modeling of continuous variables and contains six chapters. The author demonstrates the benefits of incorporating concepts of direction dependence to identify causal models. Part III of the proposed volume introduces direction dependence methods for the categorical variable case. Finally, Part IV of the proposed book is devoted to substantive theory and real-world applications and consists of four chapters. The author introduces custom dialogs and macros in SPSS to make direction dependence analysis accessible to applied empirical researchers.

Direction Dependence in Statistical Modeling: Methods of Analysis

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 Causal Inference 124

5.3.3 Applications to Multivariate Cases 125

5.4 Conclusion 125

References 126

6 Assumption Checking for Directional Causality Analyses 131
Phillip K. Wood

6.1 Epistemic Causality 135

6.1.1 Example Data Set 136

6.2 Assessment of Functional Form: Loess Regression 137

6.3 Influential and Outlying Observations 140

6.4 Directional Dependence Based on All Available Data 141

6.4.1 Studentized Deleted Residuals 143

6.4.2 Lever 143

6.4.3 DFFITS 144

6.4.4 DFBETA 145

6.4.5 Results from Influence Diagnostics 145

6.4.6 Directional Dependence Based on Factor Scores 148

6.5 Directional Dependence Based on Latent Difference Scores 149

6.6 Direction Dependence Based on State-Trait Models 153

6.7 Discussion 156

References 163

7 Complete Dependence: A Survey 167
Santi Tasena

7.1 Basic Properties 168

7.2 Measure of Complete Dependence 171

7.3 Example Calculation 177

7.4 Future Works and Open Problems 180

References 181

Part III Direction Dependence in Categorical Variables 183

8 Locating Direction Dependence Using Log-Linear Modeling, Configural Frequency Analysis, and Prediction Analysis 185
Alexander von Eye and Wolfgang Wiedermann

8.1 Specifying Directional Hypotheses in Categorical Variables 187

8.2 Types of Directional Hypotheses 192

8.2.1 Multiple Premises and Outcomes 192

8.3 Analyzing Event-Based Directional Hypotheses 193

8.3.1 Log-Linear Models of Direction Dependence 193

8.3.1.1 Identification Issues 197

8.3.2 Confirmatory Configural Frequency Analysis (CFA) of Direction Dependence 198

8.3.3 Prediction Analysis of Cross-Classifications 200

8.3.3.1 Descriptive Measures of Prediction Success 202

8.4 Data Example 203

8.4.1 Log-Linear Analysis 205

8.4.2 Configural Analysis 206

8.4.3 Prediction Analysis 208

8.5 Reversing Direction of Effect 209

8.5.1 Log-Linear Modeling of the Re-Specified Hypotheses 209

8.5.2 CFA of the Re-Specified Hypotheses 210

8.5.3 PA of the Re-Specified Hypotheses 212

8.6 Discussion 212

References 215

9 Recent Developments on Asymmetric Association Measures for Contingency Tables 219
Xiaonan Zhu, Zheng Wei, and Tonghui Wang

9.1 Introduction 219

9.2 Measures on Two-Way Contingency Tables 220

9.2.1 Functional Chi-Square Statistic 220

9.2.2 Measures of Complete Dependence 222

9.2.3 A Measure of Asymmetric Association Using Subcopula-Based Regression 223

9.3 Asymmetric Measures of Three-Way Contingency Tables 225

9.3.1 Measures of Complete Dependence for Three Way Contingency Table 225

9.3.2 Subcopula Based Measure for Three Way Contingency Table 232

9.3.3 Estimation 235

9.4 Simulation of Three-Way Contingency Tables 237

9.5 Real Data of Three-Way Contingency Tables 239

References 240

10 Analysis of Asymmetric Dependence for Three-Way Contingency Tables Using the Subcopula Approach 243
Daeyoung Kim and Zheng Wei

10.1 Introduction 243

10.2 Review on Subcopula Based Asymmetric Association Measure for Ordinal Two-Way Contingency Table 245

10.3 Measure of Asymmetric Association for Ordinal Three-Way Contingency Tables via Subcopula Regression 248

10.3.1 Subcopula Regression-Based Asymmetric Association Measures 248

10.3.2 Estimation 251

10.4 Numerical Examples 253

10.4.1 Sensitivity Analysis 253

10.4.2 Data Analysis 257

10.5 Conclusion 260

10.A Appendix 261

10.A.1 The Proof of Proposition 10.1 261

References 262

Part IV Applications and Software 265

11 Distribution-Based Causal Inference: A Review and Practical Guidance for Epidemiologists 267
Tom Rosenström and Regina García-Velázquez

11.1 Introduction 267

11.2 Direction of Dependence in Linear Regression 268

11.3 Previous Epidemiologic Applications of Distribution-Based Causal Inference 271

11.4 A Running Example: Re-Visiting the Case of Sleep Problems and Depression 273

11.5 Evaluating the Assumptions in Practical Work 274

11.5.1 Testing Linearity 275

11.5.2 Testing Non-Normality 276

11.5.3 Testing Independence 277

11.6 Distribution-Based Causality Estimates for the Running Example 278

11.7 Conducting Sensitivity Analyses 279

11.7.1 Convergent Evidence from Multiple Estimators 279

11.7.2 Simulation-Based Analysis of Robustness to Latent Confounding 279

11.7.2.1 Obtain Data-Based Parameters 281

11.7.2.2 Defining Parameters and Simulation Conditions 281

11.7.2.3 Defining the Simulation Model 282

11.7.2.4 Run Simulation and Interpret Results 283

11.8 Simulation-Based Analysis of Statistical Power 284

11.9 Triangulating Causal Inferences 288

11.10 Conclusion 291

References 292

12 Determining Causality in Relation to Early Risk Factors for ADHD: The Case of Breastfeeding Duration 295
Joel T. Nigg, Diane D. Stadler, Alexander von Eye, and Wolfgang Wiedermann

12.1 Method 298

12.1.1 Participants 298

12.1.1.1 Recruitment and Identification 298

12.1.1.2 Parental Psychopathology 299

12.1.1.3 Ethical Standards 300

12.1.2 Exclusion Criteria 300

12.1.2.1 Assessment of Breastfeeding Duration 300

12.1.3 Covariates 301

12.1.3.1 Parental Education 301

12.1.3.2 Primary Residence and Family Income 301

12.1.3.3 Parental Occupational Status 301

12.1.4 Data Reduction and Data Analysis 301

12.1.4.1 Parental ADHD 301

12.1.4.2 Data Reduction 301

12.1.4.3 Data Analysis 302

12.2 Results 304

12.2.1 Study Participant Demographic and Clinical Characteristics 304

12.3 Discussion 316

12.3.1 Limitations 317

12.3.2 Question of Causality 317

Acknowledgments 318

References 318

13 Direction of Effect Between Intimate Partner Violence and Mood Lability: A Granger Causality Model 325
G. Anne Bogat, Alytia A. Levendosky, Jade E. Kobayashi, and Alexander von Eye

13.1 Introduction 325

13.1.1 Definitions and Frequency of IPV 326

13.1.2 Depression, Mood and IPV 329

13.1.2.1 Depression and IPV 329

13.1.2.2 Mood and IPV 330

13.1.3 Summary 332

13.2 Methods 333

13.2.1 Participants 333

13.2.2 Measures 333

13.2.2.1 Daily Diary Questions 333

13.2.3 Procedures 334

13.3 Results 334

13.3.1 Data Consolidation 334

13.3.2 Descriptive Statistics 335

13.3.3 Model Development 335

13.3.4 Granger Causality Analyses 337

13.4 Discussion 341

References 343

14 On the Causal Relation of Academic Achievement and Intrinsic Motivation: An Application of Direction Dependence Analysis Using SPSS Custom Dialogs 351
Xintong Li and Wolfgang Wiedermann

14.1 Direction of Dependence in Linear Regression 352

14.1.1 Distributional Properties of x and y 353

14.1.2 Distributional Properties of ex and ey 354

14.1.3 Independence of Error Terms with Predictor Variable 355

14.1.4 DDA in Confounded Models 356

14.1.5 DDA in Multiple Linear Regression Models 356

14.2 The Causal Relation of Intrinsic Motivation and Academic Achievement 359

14.2.1 High School Longitudinal Study 2009 360

14.3 Direction Dependence Analysis Using SPSS 363

14.3.1 Variable Distributions and Assumption Checks 363

14.3.2 Residual Distributions 366

14.3.3 Independence Properties 368

14.3.4 Summary of DDA Results 369

14.4 Conclusions 371

14.4.1 Extensions and Future Work 372

References 372

Author Index 379

Subject Index 395