Some Results on the Estimation of Covariance Structure Models PDF Download
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Author: Jeffrey S. Tanaka
Publisher:
ISBN:
Category :
Languages : en
Pages : 180
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Book Description
Author: Jeffrey S. Tanaka
Publisher:
ISBN:
Category :
Languages : en
Pages : 180
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Book Description
Author: Jeffrey Scott Tanaka
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 396
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Book Description
Author: Ralph Lawrence Kodell
Publisher:
ISBN:
Category :
Languages : en
Pages : 146
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Book Description
Author: J. Scott Long
Publisher: SAGE Publications, Incorporated
ISBN:
Category : Mathematics
Languages : en
Pages : 104
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Book Description
While many readers may be unfamiliar with the full complexity of the covariance structure model, many may have mastered at least one of its two components - each of which is a powerful and well-known statistical technique in its own right. The first is the confirmatory factor model frequently used in psychometrics; the second, the structural equation model, is familiar to econometricians. The discussion in this volume will be particularly useful for estimating models with equality constraints and correlated errors across some but not all equations. The final chapter includes a guide to appropriate software packages.
Author: Randall E. Schumacker
Publisher: Routledge
ISBN: 1351562630
Category : Psychology
Languages : en
Pages : 276
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Book Description
This volume provides a comprehensive presentation of the various procedures currently available for testing interaction and nonlinear effects in structural equation modeling. By focusing on various software applications, the reader should quickly be able to incorporate one of the procedures into testing interaction or nonlinear effects in their own model. Although every attempt is made to keep mathematical details to a minimum, it is assumed that the reader has mastered the equivalent of a graduate-level multivariate statistics course which includes adequate coverage of structural equation modeling. This book will be of interest to researchers and practitioners in education and the social sciences.
Author: Alberto Maydeu-Olivares
Publisher:
ISBN:
Category :
Languages : en
Pages : 36
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Book Description
Author: Aygul Zagidullina
Publisher: Springer Nature
ISBN: 3030800652
Category : Business & Economics
Languages : en
Pages : 123
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Book Description
This book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime that better fits the big data context. It draws attention to the deficiencies of standard statistical tools when used in the high-dimensional setting, and introduces the basic concepts and major results related to spectral statistics and random matrix theory under high-dimensional asymptotics in an understandable and reader-friendly way. The aim of this book is to inspire applied statisticians, econometricians, and machine learning practitioners who analyze high-dimensional data to apply the recent developments in their work.
Author: Jeffrey K. Speakes
Publisher:
ISBN:
Category :
Languages : en
Pages : 366
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Book Description
Author: Jhareswar Maiti
Publisher: CRC Press
ISBN: 1000618390
Category : Mathematics
Languages : en
Pages : 637
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Book Description
The book focuses on problem solving for practitioners and model building for academicians under multivariate situations. This book helps readers in understanding the issues, such as knowing variability, extracting patterns, building relationships, and making objective decisions. A large number of multivariate statistical models are covered in the book. The readers will learn how a practical problem can be converted to a statistical problem and how the statistical solution can be interpreted as a practical solution. Key features: Links data generation process with statistical distributions in multivariate domain Provides step by step procedure for estimating parameters of developed models Provides blueprint for data driven decision making Includes practical examples and case studies relevant for intended audiences The book will help everyone involved in data driven problem solving, modeling and decision making.
Author: Bang Quan Zheng
Publisher:
ISBN:
Category :
Languages : en
Pages : 39
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Book Description
Does Reweighted Least Squares (RLS) perform better in small samples than maximum likelihood (ML) for mean and covariance structure? ML statistics in covariance structure analysis are based on the asymptotic normality assumption; however, actual applications of structural equation modeling (SEM) in social and behavioral science research usually involve small samples. It has been found that chi-square tests often incorrectly over-reject the null hypothesis: = ( ), because when sample is small the sample covariance matrix becomes ill-conditioned and entails unstable estimates. In certain SEM models, the vector of parameter must contain both means, variances and covariances. Yet, whether RLS also works in mean and covariance structure remains unexamined. This research is an extended examination of reweighted least squares in mean and covariance structure. Specifically, we replace biased covariance matrix in traditional GLS function (Browne, 1974) with the unbiased sample covariance matrix that derives from ML estimation. Moreover, under the assumption of multivariate normality, a Monte Carlo simulation study was carried out to examine the statistical performance as compared with ML methods in different sample sizes. Based on empirical rejection frequencies and empirical averages of test statistic, this study shows that RLS performs much better than ML in mean and covariance structure models when sample sizes are small.
