Direction Dependence in Statistical Modeling

Direction Dependence in Statistical Modeling
Author: Wolfgang Wiedermann
Publisher: John Wiley & Sons
Total Pages: 432
Release: 2020-12-03
Genre: Mathematics
ISBN: 1119523079

Download Direction Dependence in Statistical Modeling Book in PDF, Epub and Kindle

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hypotheses A presentation of the development of methods for direction dependence analysis together with recent novel, unpublished software implementations A review of methods of direction dependence following the copula-based tradition of Sungur and Kim A presentation of extensions of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
Author: Wolfgang Wiedermann
Publisher: John Wiley & Sons
Total Pages: 432
Release: 2020-11-24
Genre: Mathematics
ISBN: 1119523141

Download Direction Dependence in Statistical Modeling Book in PDF, Epub and Kindle

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
Author: Mark Stemmler
Publisher: Springer
Total Pages: 385
Release: 2015-10-19
Genre: Social Science
ISBN: 3319205854

Download Dependent Data in Social Sciences Research Book in PDF, Epub and Kindle

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

Statistical Modeling and Analysis for Complex Data Problems
Author: Pierre Duchesne
Publisher: Springer Science & Business Media
Total Pages: 330
Release: 2005-12-05
Genre: Mathematics
ISBN: 0387245553

Download Statistical Modeling and Analysis for Complex Data Problems Book in PDF, Epub and Kindle

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
Author: Alexander von Eye
Publisher: Cambridge University Press
Total Pages: 191
Release: 2023-06-30
Genre: Psychology
ISBN: 1009322184

Download The General Linear Model Book in PDF, Epub and Kindle

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
Author: Edoardo M. Airoldi
Publisher: Springer
Total Pages: 204
Release: 2008-04-12
Genre: Computers
ISBN: 3540731334

Download Statistical Network Analysis: Models, Issues, and New Directions Book in PDF, Epub and Kindle

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.

Applied Linear Statistical Models

Applied Linear Statistical Models
Author: Michael H. Kutner
Publisher: McGraw-Hill/Irwin
Total Pages: 1396
Release: 2005
Genre: Mathematics
ISBN: 9780072386882

Download Applied Linear Statistical Models Book in PDF, Epub and Kindle

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.

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

Advances in Econometrics, Operational Research, Data Science and Actuarial Studies
Author: M. Kenan Terzioğlu
Publisher: Springer Nature
Total Pages: 607
Release: 2022-01-17
Genre: Business & Economics
ISBN: 3030852547

Download Advances in Econometrics, Operational Research, Data Science and Actuarial Studies Book in PDF, Epub and Kindle

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 Directional Statistics

Applied Directional Statistics
Author: Christophe Ley
Publisher: CRC Press
Total Pages: 400
Release: 2018-09-03
Genre: Mathematics
ISBN: 1351856529

Download Applied Directional Statistics Book in PDF, Epub and Kindle

This book collects important advances in methodology and data analysis for directional statistics. It is the companion book of the more theoretical treatment presented in Modern Directional Statistics (CRC Press, 2017). The field of directional statistics has received a lot of attention due to demands from disciplines such as life sciences or machine learning, the availability of massive data sets requiring adapted statistical techniques, and technological advances. This book covers important progress in bioinformatics, biology, astrophysics, oceanography, environmental sciences, earth sciences, machine learning and social sciences.

Animal Movement

Animal Movement
Author: Mevin B. Hooten
Publisher: CRC Press
Total Pages: 306
Release: 2017-03-16
Genre: Mathematics
ISBN: 1466582154

Download Animal Movement Book in PDF, Epub and Kindle

The study of animal movement has always been a key element in ecological science, because it is inherently linked to critical processes that scale from individuals to populations and communities to ecosystems. Rapid improvements in biotelemetry data collection and processing technology have given rise to a variety of statistical methods for characterizing animal movement. The book serves as a comprehensive reference for the types of statistical models used to study individual-based animal movement. Animal Movement is an essential reference for wildlife biologists, quantitative ecologists, and statisticians who seek a deeper understanding of modern animal movement models. A wide variety of modeling approaches are reconciled in the book using a consistent notation. Models are organized into groups based on how they treat the underlying spatio-temporal process of movement. Connections among approaches are highlighted to allow the reader to form a broader view of animal movement analysis and its associations with traditional spatial and temporal statistical modeling. After an initial overview examining the role that animal movement plays in ecology, a primer on spatial and temporal statistics provides a solid foundation for the remainder of the book. Each subsequent chapter outlines a fundamental type of statistical model utilized in the contemporary analysis of telemetry data for animal movement inference. Descriptions begin with basic traditional forms and sequentially build up to general classes of models in each category. Important background and technical details for each class of model are provided, including spatial point process models, discrete-time dynamic models, and continuous-time stochastic process models. The book also covers the essential elements for how to accommodate multiple sources of uncertainty, such as location error and latent behavior states. In addition to thorough descriptions of animal movement models, differences and connections are also emphasized to provide a broader perspective of approaches.