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Generalized Structured Component Analysis

Generalized Structured Component Analysis
Author: Heungsun Hwang
Publisher: CRC Press
Total Pages: 346
Release: 2014-12-11
Genre: Mathematics
ISBN: 146659294X
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Developed by the authors, generalized structured component analysis is an alternative to two longstanding approaches to structural equation modeling: covariance structure analysis and partial least squares path modeling. Generalized structured component analysis allows researchers to evaluate the adequacy of a model as a whole, compare a model to alternative specifications, and conduct complex analyses in a straightforward manner. Generalized Structured Component Analysis: A Component-Based Approach to Structural Equation Modeling provides a detailed account of this novel statistical methodology and its various extensions. The authors present the theoretical underpinnings of generalized structured component analysis and demonstrate how it can be applied to various empirical examples. The book enables quantitative methodologists, applied researchers, and practitioners to grasp the basic concepts behind this new approach and apply it to their own research. The book emphasizes conceptual discussions throughout while relegating more technical intricacies to the chapter appendices. Most chapters compare generalized structured component analysis to partial least squares path modeling to show how the two component-based approaches differ when addressing an identical issue. The authors also offer a free, online software program (GeSCA) and an Excel-based software program (XLSTAT) for implementing the basic features of generalized structured component analysis.

Functional Generalized Structured Component Analysis

Functional Generalized Structured Component Analysis
Author: Hye Won Suk
Publisher:
Total Pages:
Release: 2013
Genre:
ISBN:
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"The invention of sophisticated measurement tools, such as motion capture devices, handheld computers, Bluetooth devices, eye-trackers, and brain scanners, has facilitated the collection of functional data that can be considered to arise from an underlying smooth function varying over a continuum such as time and space. Functional data analysis (FDA) is an emerging branch of statistics, which develops and applies statistical methods for the analysis of such types of data. Various FDA methods have been proposed by extending traditional multivariate statistical methods to accommodate functional data. Nonetheless, there has been little attempt to develop functional extensions of structural equation modeling (SEM), in spite of the remarkable popularity of SEM in various disciplines due to its flexibility of modeling complex relationships among observed and latent variables. This thesis thus aims to propose a general framework for functional SEM, called functional generalized structured component analysis (functional GSCA), to examine a variety of hypothesized relationships among observed and latent variables, while permitting observed variables to be functional rather than multivariate. The thesis begins by describing GSCA and penalized least squares smoothing as the two basic building blocks of the proposed method. Subsequently, it provides the technical details of the proposed method. The model for functional GSCA is provided and a penalized least squares criterion is developed for parameter estimation, which is minimized by an alternating penalized least squares algorithm. The thesis also demonstrates the usefulness of the proposed method by analyzing synthetic and real data sets. It concludes with discussions on limitations and possible extensions of the proposed method." --

Models for Data Analysis

Models for Data Analysis
Author: Eugenio Brentari
Publisher: Springer Nature
Total Pages: 320
Release: 2023-02-20
Genre: Mathematics
ISBN: 3031158857
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The 49Th Scientific meeting of the Italian Statistical Society was held in June 2018 in Palermo, with more than 450 attendants. There were plenary sessions as well as specialized and solicited and contributed sessions. This volume collects a selection of twenty extended contributions covering a wide area of applied and theoretical issues, according to the modern trends in statistical sciences. Only to mention some topics, there are papers on modern textual analysis, sensorial analysis, social inequalities, themes on demography, modern modeling of functional data and high dimensional data, and many other topics. This volume is addressed to academics, PhD students, professionals and researchers in applied and theoretical statistical models for data analysis.

Composite-Based Structural Equation Modeling

Composite-Based Structural Equation Modeling
Author: Jörg Henseler
Publisher: Guilford Publications
Total Pages: 387
Release: 2020-12-24
Genre: Social Science
ISBN: 1462545610
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This book presents powerful tools for integrating interrelated composites--such as capabilities, policies, treatments, indices, and systems--into structural equation modeling (SEM). Jörg Henseler introduces the types of research questions that can be addressed with composite-based SEM and explores the differences between composite- and factor-based SEM, variance- and covariance-based SEM, and emergent and latent variables. Using rich illustrations and walked-through data sets, the book covers how to specify, identify, estimate, and assess composite models using partial least squares path modeling, maximum likelihood, and other estimators, as well as how to interpret findings and report the results. Advanced topics include confirmatory composite analysis, mediation analysis, second-order constructs, interaction effects, and importance–performance analysis. Most chapters conclude with software tutorials for ADANCO and the R package cSEM. The companion website includes data files and syntax for the book's examples, along with presentation slides.

Constrained Principal Component Analysis and Related Techniques

Constrained Principal Component Analysis and Related Techniques
Author: Yoshio Takane
Publisher: CRC Press
Total Pages: 244
Release: 2016-04-19
Genre: Mathematics
ISBN: 1466556684
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In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data. How can regression analysis and PCA be combined in a beneficial way? Why and when is it a good idea to combine them? Wha

Generalized Principal Component Analysis

Generalized Principal Component Analysis
Author: René Vidal
Publisher: Springer
Total Pages: 590
Release: 2016-04-11
Genre: Science
ISBN: 0387878114
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This book provides a comprehensive introduction to the latest advances in the mathematical theory and computational tools for modeling high-dimensional data drawn from one or multiple low-dimensional subspaces (or manifolds) and potentially corrupted by noise, gross errors, or outliers. This challenging task requires the development of new algebraic, geometric, statistical, and computational methods for efficient and robust estimation and segmentation of one or multiple subspaces. The book also presents interesting real-world applications of these new methods in image processing, image and video segmentation, face recognition and clustering, and hybrid system identification etc. This book is intended to serve as a textbook for graduate students and beginning researchers in data science, machine learning, computer vision, image and signal processing, and systems theory. It contains ample illustrations, examples, and exercises and is made largely self-contained with three Appendices which survey basic concepts and principles from statistics, optimization, and algebraic-geometry used in this book. René Vidal is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University. Yi Ma is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University. S. Shankar Sastry is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley.

Issues in Psychology and Psychiatry Research and Practice: 2013 Edition

Issues in Psychology and Psychiatry Research and Practice: 2013 Edition
Author:
Publisher: ScholarlyEditions
Total Pages: 1026
Release: 2013-05-01
Genre: Psychology
ISBN: 1490108017
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Issues in Psychology and Psychiatry Research and Practice: 2013 Edition is a ScholarlyEditions™ book that delivers timely, authoritative, and comprehensive information about Additional Research. The editors have built Issues in Psychology and Psychiatry Research and Practice: 2013 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Additional Research in this book to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Psychology and Psychiatry Research and Practice: 2013 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

Functional Data Analysis with R

Functional Data Analysis with R
Author: Ciprian M. Crainiceanu
Publisher: CRC Press
Total Pages: 338
Release: 2024-03-11
Genre: Mathematics
ISBN: 1003856306
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Emerging technologies generate data sets of increased size and complexity that require new or updated statistical inferential methods and scalable, reproducible software. These data sets often involve measurements of a continuous underlying process, and benefit from a functional data perspective. Functional Data Analysis with R presents many ideas for handling functional data including dimension reduction techniques, smoothing, functional regression, structured decompositions of curves, and clustering. The idea is for the reader to be able to immediately reproduce the results in the book, implement these methods, and potentially design new methods and software that may be inspired by these approaches. Features: Functional regression models receive a modern treatment that allows extensions to many practical scenarios and development of state-of-the-art software. The connection between functional regression, penalized smoothing, and mixed effects models is used as the cornerstone for inference. Multilevel, longitudinal, and structured functional data are discussed with emphasis on emerging functional data structures. Methods for clustering functional data before and after smoothing are discussed. Multiple new functional data sets with dense and sparse sampling designs from various application areas are presented, including the NHANES linked accelerometry and mortality data, COVID-19 mortality data, CD4 counts data, and the CONTENT child growth study. Step-by-step software implementations are included, along with a supplementary website (www.FunctionalDataAnalysis.com) featuring software, data, and tutorials. More than 100 plots for visualization of functional data are presented. Functional Data Analysis with R is primarily aimed at undergraduate, master's, and PhD students, as well as data scientists and researchers working on functional data analysis. The book can be read at different levels and combines state-of-the-art software, methods, and inference. It can be used for self-learning, teaching, and research, and will particularly appeal to anyone who is interested in practical methods for hands-on, problem-forward functional data analysis. The reader should have some basic coding experience, but expertise in R is not required.