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Direction Dependence in Statistical Modeling

Direction Dependence in Statistical Modeling PDF Author: Wolfgang Wiedermann
Publisher: John Wiley & Sons
ISBN: 1119523141
Category : Mathematics
Languages : en
Pages : 432

Book Description
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hypotheses A presentation of the development of methods for direction dependence analysis together with recent novel, unpublished software implementations A review of methods of direction dependence following the copula-based tradition of Sungur and Kim A presentation of extensions of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.

Direction Dependence in Statistical Modeling

Direction Dependence in Statistical Modeling PDF Author: Wolfgang Wiedermann
Publisher: John Wiley & Sons
ISBN: 1119523141
Category : Mathematics
Languages : en
Pages : 432

Book Description
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.

Direction Dependence in Statistical Modeling

Direction Dependence in Statistical Modeling PDF Author: Wolfgang Wiedermann
Publisher: John Wiley & Sons
ISBN: 1119523079
Category : Mathematics
Languages : en
Pages : 432

Book Description
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.

Dependent Data in Social Sciences Research

Dependent Data in Social Sciences Research PDF Author: Mark Stemmler
Publisher: Springer
ISBN: 3319205854
Category : Social Science
Languages : en
Pages : 385

Book Description
This volume presents contributions on handling data in which the postulate of independence in the data matrix is violated. When this postulate is violated and when the methods assuming independence are still applied, the estimated parameters are likely to be biased, and statistical decisions are very likely to be incorrect. Problems associated with dependence in data have been known for a long time, and led to the development of tailored methods for the analysis of dependent data in various areas of statistical analysis. These methods include, for example, methods for the analysis of longitudinal data, corrections for dependency, and corrections for degrees of freedom. This volume contains the following five sections: growth curve modeling, directional dependence, dyadic data modeling, item response modeling (IRT), and other methods for the analysis of dependent data (e.g., approaches for modeling cross-section dependence, multidimensional scaling techniques, and mixed models). Researchers and graduate students in the social and behavioral sciences, education, econometrics, and medicine will find this up-to-date overview of modern statistical approaches for dealing with problems related to dependent data particularly useful.

Dependent Data in Social Sciences Research

Dependent Data in Social Sciences Research PDF Author: Mark Stemmler
Publisher: Springer Nature
ISBN: 3031563182
Category :
Languages : en
Pages : 785

Book Description


Statistical Modeling and Analysis for Complex Data Problems

Statistical Modeling and Analysis for Complex Data Problems PDF Author: Pierre Duchesne
Publisher: Springer Science & Business Media
ISBN: 0387245553
Category : Mathematics
Languages : en
Pages : 330

Book Description
This book reviews some of today’s more complex problems, and reflects some of the important research directions in the field. Twenty-nine authors – largely from Montreal’s GERAD Multi-University Research Center and who work in areas of theoretical statistics, applied statistics, probability theory, and stochastic processes – present survey chapters on various theoretical and applied problems of importance and interest to researchers and students across a number of academic domains.

The General Linear Model

The General Linear Model PDF Author: Alexander von Eye
Publisher: Cambridge University Press
ISBN: 1009322184
Category : Psychology
Languages : en
Pages : 191

Book Description
General Linear Model methods are the most widely used in data analysis in applied empirical research. Still, there exists no compact text that can be used in statistics courses and as a guide in data analysis. This volume fills this void by introducing the General Linear Model (GLM), whose basic concept is that an observed variable can be explained from weighted independent variables plus an additive error term that reflects imperfections of the model and measurement error. It also covers multivariate regression, analysis of variance, analysis under consideration of covariates, variable selection methods, symmetric regression, and the recently developed methods of recursive partitioning and direction dependence analysis. Each method is formally derived and embedded in the GLM, and characteristics of these methods are highlighted. Real-world data examples illustrate the application of each of these methods, and it is shown how results can be interpreted.

Statistical Network Analysis: Models, Issues, and New Directions

Statistical Network Analysis: Models, Issues, and New Directions PDF Author: Edoardo M. Airoldi
Publisher: Springer
ISBN: 3540731334
Category : Computers
Languages : en
Pages : 204

Book Description
This book constitutes the thoroughly refereed post-proceedings of the International Workshop on Statistical Network Analysis: Models, Issues, and New Directions held in Pittsburgh, PA, USA in June 2006 as associated event of the 23rd International Conference on Machine Learning, ICML 2006. It covers probabilistic methods for network analysis, paying special attention to model design and computational issues of learning and inference.

Advances in Econometrics, Operational Research, Data Science and Actuarial Studies

Advances in Econometrics, Operational Research, Data Science and Actuarial Studies PDF Author: M. Kenan Terzioğlu
Publisher: Springer Nature
ISBN: 3030852547
Category : Business & Economics
Languages : en
Pages : 607

Book Description
This volume presents techniques and theories drawn from mathematics, statistics, computer science, and information science to analyze problems in business, economics, finance, insurance, and related fields. The authors present proposals for solutions to common problems in related fields. To this end, they are showing the use of mathematical, statistical, and actuarial modeling, and concepts from data science to construct and apply appropriate models with real-life data, and employ the design and implementation of computer algorithms to evaluate decision-making processes. This book is unique as it associates data science - data-scientists coming from different backgrounds - with some basic and advanced concepts and tools used in econometrics, operational research, and actuarial sciences. It, therefore, is a must-read for scholars, students, and practitioners interested in a better understanding of the techniques and theories of these fields.

Applied Linear Statistical Models

Applied Linear Statistical Models PDF Author: Michael H. Kutner
Publisher: McGraw-Hill/Irwin
ISBN: 9780072386882
Category : Mathematics
Languages : en
Pages : 1396

Book Description
Linear regression with one predictor variable; Inferences in regression and correlation analysis; Diagnosticis and remedial measures; Simultaneous inferences and other topics in regression analysis; Matrix approach to simple linear regression analysis; Multiple linear regression; Nonlinear regression; Design and analysis of single-factor studies; Multi-factor studies; Specialized study designs.

Advancing Developmental Science

Advancing Developmental Science PDF Author: Anthony S. Dick
Publisher: Taylor & Francis
ISBN: 1351704567
Category : Family & Relationships
Languages : en
Pages : 271

Book Description
Advancing Developmental Science reviews the state-of-the-science in theoretical, methodological, and topical research, with a unique focus on the scholarship that developed within a process-relational framework.