Direction Dependence In Statistical Modeling

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Direction Dependence in Statistical Modeling

Wolfgang Wiedermann,Daeyoung Kim,Engin A. Sungur,Alexander von Eye
Direction Dependence in Statistical Modeling Book PDF

Author : Wolfgang Wiedermann,Daeyoung Kim,Engin A. Sungur,Alexander von Eye
Publisher : John Wiley & Sons
Page : 432 pages
File Size : 45,6 Mb
Release : 2020-12-03
Category : Mathematics
ISBN : 9781119523079

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Direction Dependence in Statistical Modeling by Wolfgang Wiedermann,Daeyoung Kim,Engin A. Sungur,Alexander von Eye Pdf

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hypotheses A presentation of the development of methods for direction dependence analysis together with recent novel, unpublished software implementations A review of methods of direction dependence following the copula-based tradition of Sungur and Kim A presentation of extensions of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

Wolfgang Wiedermann,Daeyoung Kim,Engin A. Sungur,Alexander von Eye
Direction Dependence in Statistical Modeling Book PDF

Author : Wolfgang Wiedermann,Daeyoung Kim,Engin A. Sungur,Alexander von Eye
Publisher : John Wiley & Sons
Page : 432 pages
File Size : 44,6 Mb
Release : 2020-11-09
Category : Mathematics
ISBN : 9781119523130

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Direction Dependence in Statistical Modeling by Wolfgang Wiedermann,Daeyoung Kim,Engin A. Sungur,Alexander von Eye Pdf

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

Mark Stemmler,Alexander von Eye,Wolfgang Wiedermann
Dependent Data in Social Sciences Research Book PDF

Author : Mark Stemmler,Alexander von Eye,Wolfgang Wiedermann
Publisher : Springer
Page : 385 pages
File Size : 41,8 Mb
Release : 2015-10-19
Category : Social Science
ISBN : 9783319205854

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Dependent Data in Social Sciences Research by Mark Stemmler,Alexander von Eye,Wolfgang Wiedermann Pdf

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.

Statistical Modeling and Analysis for Complex Data Problems

Pierre Duchesne,Bruno Rémillard
Statistical Modeling and Analysis for Complex Data Problems Book PDF

Author : Pierre Duchesne,Bruno Rémillard
Publisher : Springer Science & Business Media
Page : 330 pages
File Size : 46,5 Mb
Release : 2005-12-05
Category : Mathematics
ISBN : 9780387245553

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Statistical Modeling and Analysis for Complex Data Problems by Pierre Duchesne,Bruno Rémillard Pdf

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

Alexander von Eye,Wolfgang Wiedermann
The General Linear Model Book PDF

Author : Alexander von Eye,Wolfgang Wiedermann
Publisher : Cambridge University Press
Page : 191 pages
File Size : 46,9 Mb
Release : 2023-06-30
Category : Psychology
ISBN : 9781009322188

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The General Linear Model by Alexander von Eye,Wolfgang Wiedermann Pdf

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.

Linear Models in Statistics

Alvin C. Rencher,G. Bruce Schaalje
Linear Models in Statistics Book PDF

Author : Alvin C. Rencher,G. Bruce Schaalje
Publisher : John Wiley & Sons
Page : 690 pages
File Size : 40,9 Mb
Release : 2008-01-07
Category : Mathematics
ISBN : 9780470192603

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Linear Models in Statistics by Alvin C. Rencher,G. Bruce Schaalje Pdf

The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.

Applied Linear Statistical Models

Michael H. Kutner
Applied Linear Statistical Models Book PDF

Author : Michael H. Kutner
Publisher : McGraw-Hill Education
Page : 1396 pages
File Size : 52,9 Mb
Release : 2005
Category : Analysis of variance
ISBN : 0071122214

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Applied Linear Statistical Models by Michael H. Kutner Pdf

Applied Linear Statistical Models 5e is the long established leading authoritative text and reference on statistical modeling. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Notes" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, and projects are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and where methods can be automated within software without loss of understanding, it is so done.

Statistical Network Analysis: Models, Issues, and New Directions

Edoardo M. Airoldi,David M. Blei,Stephen E. Fienberg,Anna Goldenberg,Eric P. Xing,Alice X. Zheng
Statistical Network Analysis Models Issues and New Directions Book PDF

Author : Edoardo M. Airoldi,David M. Blei,Stephen E. Fienberg,Anna Goldenberg,Eric P. Xing,Alice X. Zheng
Publisher : Springer
Page : 200 pages
File Size : 50,8 Mb
Release : 2008-04-12
Category : Computers
ISBN : 9783540731337

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Statistical Network Analysis: Models, Issues, and New Directions by Edoardo M. Airoldi,David M. Blei,Stephen E. Fienberg,Anna Goldenberg,Eric P. Xing,Alice X. Zheng Pdf

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.

Interpretable Machine Learning

Christoph Molnar
Interpretable Machine Learning Book PDF

Author : Christoph Molnar
Publisher : Lulu.com
Page : 320 pages
File Size : 45,8 Mb
Release : 2020
Category : Artificial intelligence
ISBN : 9780244768522

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Interpretable Machine Learning by Christoph Molnar Pdf

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

The General Linear Model

Alexander von Eye,Wolfgang Wiedermann
The General Linear Model Book PDF

Author : Alexander von Eye,Wolfgang Wiedermann
Publisher : Cambridge University Press
Page : 191 pages
File Size : 46,6 Mb
Release : 2023-06-30
Category : Psychology
ISBN : 9781009322171

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The General Linear Model by Alexander von Eye,Wolfgang Wiedermann Pdf

Introduces the General Linear Model and the methods that can be derived from it, including direction dependence analysis.

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

M. Kenan Terzioğlu
Advances in Econometrics Operational Research Data Science and Actuarial Studies Book PDF

Author : M. Kenan Terzioğlu
Publisher : Springer Nature
Page : 607 pages
File Size : 43,5 Mb
Release : 2022-01-17
Category : Business & Economics
ISBN : 9783030852542

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Advances in Econometrics, Operational Research, Data Science and Actuarial Studies by M. Kenan Terzioğlu Pdf

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.

Statistical Power Analysis for the Behavioral Sciences

Jacob Cohen
Statistical Power Analysis for the Behavioral Sciences Book PDF

Author : Jacob Cohen
Publisher : Routledge
Page : 625 pages
File Size : 50,9 Mb
Release : 2013-05-13
Category : Psychology
ISBN : 9781134742776

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Statistical Power Analysis for the Behavioral Sciences by Jacob Cohen Pdf

Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: * a chapter covering power analysis in set correlation and multivariate methods; * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and; * expanded power and sample size tables for multiple regression/correlation.

Advancing Developmental Science

Anthony S. Dick,Ulrich Müller
Advancing Developmental Science Book PDF

Author : Anthony S. Dick,Ulrich Müller
Publisher : Taylor & Francis
Page : 271 pages
File Size : 46,7 Mb
Release : 2017-07-06
Category : FAMILY & RELATIONSHIPS
ISBN : 9781351704564

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Advancing Developmental Science by Anthony S. Dick,Ulrich Müller Pdf

Developmental science is an interdisciplinary scientific field dedicated to describing, understanding, and explaining change in behavior across the lifespan and the psychological, environmental, and biological processes that co-determine this change during the organism’s development. Developmental science is thus a broad discipline that lies at the intersection of psychology, biology, sociology, anthropology and other allied disciplines. Advancing Developmental Science: Philosophy, Theory, and Method reflects this broad view of developmental science, and reviews the philosophical, theoretical, and methodological issues facing the field. It does so within the Process-Relational paradigm, as described by developmentalist Willis Overton over the course of his career. Within that framework, this book explores development in a number of specific cognitive, neurobiological, and social domains, and provides students and researchers with a comprehensive suite of conceptual and methodological tools to describe, explain, and optimize intraindividual change across the lifespan.

Statistical Modelling in GLIM 4

Murray A. Aitkin,Brian Francis,John Hinde
Statistical Modelling in GLIM 4 Book PDF

Author : Murray A. Aitkin,Brian Francis,John Hinde
Publisher : Oxford University Press, USA
Page : 584 pages
File Size : 48,6 Mb
Release : 2005
Category : Computers
ISBN : 0198524137

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Statistical Modelling in GLIM 4 by Murray A. Aitkin,Brian Francis,John Hinde Pdf

"This text examines the theory of statistical modelling with generalised linear models. It also looks at applications of the theory to practical problems, using the GLIM4 package"--Provided by publisher.

Mixed Effects Models for Complex Data

Lang Wu
Mixed Effects Models for Complex Data Book PDF

Author : Lang Wu
Publisher : CRC Press
Page : 431 pages
File Size : 51,7 Mb
Release : 2009-11-11
Category : Mathematics
ISBN : 1420074083

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Mixed Effects Models for Complex Data by Lang Wu Pdf

Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.