High Breakdown Inference for Mixed Linear Models

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ISBN 13 :
Total Pages : 127 pages
Book Rating : 4.:/5 (254 download)

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Book Synopsis High Breakdown Inference for Mixed Linear Models by : Samuel Copt

Download or read book High Breakdown Inference for Mixed Linear Models written by Samuel Copt and published by . This book was released on 2004 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: Les modèles linéaires mixtes sont utilisés pour analyser les données issues de recherches dans de nombreux domaines des sciences humaines et sociales. Pour estimer ces modèles et tester des hypothèses, nous proposons dans ce travail une approche dite robuste qui protège les analyses de biais potentiels dus à la présence dans les échantillons d'une minorité de données atypiques. Nous partons d'une formalisation multivariée de ces modèles et proposons un estimateur robuste appartenant à la classe des estimateurs S (Rousseeuw et Yohai, 1984) dont nous dérivons les propriétés asymptotiques. Le comportement de cet estimateur est illustré à l'aide de simulations ainsi que par l'analyse de quatre jeux de données réels. Nous proposons également une alternative robuste au test classique de Fisher. Il s'agit d'un test du score robuste proposé par Héritier et Ronchetti (1994).

High Breakdown Inference in the Mixed Linear Model

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ISBN 13 :
Total Pages : 35 pages
Book Rating : 4.:/5 (853 download)

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Book Synopsis High Breakdown Inference in the Mixed Linear Model by : Samuel Copt

Download or read book High Breakdown Inference in the Mixed Linear Model written by Samuel Copt and published by . This book was released on 2003 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Mixed Effects Models for Complex Data

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Publisher : CRC Press
ISBN 13 : 9781420074086
Total Pages : 431 pages
Book Rating : 4.0/5 (74 download)

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Book Synopsis Mixed Effects Models for Complex Data by : Lang Wu

Download or read book Mixed Effects Models for Complex Data written by Lang Wu and published by CRC Press. This book was released on 2009-11-11 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Linear Models

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Publisher : John Wiley & Sons
ISBN 13 : 9780470377970
Total Pages : 288 pages
Book Rating : 4.3/5 (779 download)

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Book Synopsis Linear Models by : Brenton R. Clarke

Download or read book Linear Models written by Brenton R. Clarke and published by John Wiley & Sons. This book was released on 2008-09-19 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: An insightful approach to the analysis of variance in the study of linear models Linear Models explores the theory of linear models and the dynamic relationships that these models have with Analysis of Variance (ANOVA), experimental design, and random and mixed-model effects. This one-of-a-kind book emphasizes an approach that clearly explains the distribution theory of linear models and experimental design starting from basic mathematical concepts in linear algebra. The author begins with a presentation of the classic fixed-effects linear model and goes on to illustrate eight common linear models, along with the value of their use in statistics. From this foundation, subsequent chapters introduce concepts pertaining to the linear model, starting with vector space theory and the theory of least-squares estimation. An outline of the Helmert matrix is also presented, along with a thorough explanation of how the ANOVA is created in both typical two-way and higher layout designs, ultimately revealing the distribution theory. Other important topics covered include: Vector space theory The theory of least squares estimation Gauss-Markov theorem Kronecker products Diagnostic and robust methods for linear models Likelihood approaches to estimation A discussion of Bayesian theory is also included for purposes of comparison and contrast, and numerous illustrative exercises assist the reader with uncovering the nature of the models, using both classic and new data sets. Requiring only a working knowledge of basic probability and statistical inference, Linear Models is a valuable book for courses on linear models at the upper-undergraduate and graduate levels. It is also an excellent reference for practitioners who use linear models to conduct research in the fields of econometrics, psychology, sociology, biology, and agriculture.

Robust Methods in Biostatistics

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Publisher : John Wiley & Sons
ISBN 13 : 9780470740545
Total Pages : 292 pages
Book Rating : 4.7/5 (45 download)

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Book Synopsis Robust Methods in Biostatistics by : Stephane Heritier

Download or read book Robust Methods in Biostatistics written by Stephane Heritier and published by John Wiley & Sons. This book was released on 2009-05-11 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the postulated models as it is the case for example with outliers. Robust Methods in Biostatistics proposes robust alternatives to common methods used in statistics in general and in biostatistics in particular and illustrates their use on many biomedical datasets. The methods introduced include robust estimation, testing, model selection, model check and diagnostics. They are developed for the following general classes of models: Linear regression Generalized linear models Linear mixed models Marginal longitudinal data models Cox survival analysis model The methods are introduced both at a theoretical and applied level within the framework of each general class of models, with a particular emphasis put on practical data analysis. This book is of particular use for research students,applied statisticians and practitioners in the health field interested in more stable statistical techniques. An accompanying website provides R code for computing all of the methods described, as well as for analyzing all the datasets used in the book.

Robust and Multivariate Statistical Methods

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Publisher : Springer Nature
ISBN 13 : 3031226879
Total Pages : 500 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Robust and Multivariate Statistical Methods by : Mengxi Yi

Download or read book Robust and Multivariate Statistical Methods written by Mengxi Yi and published by Springer Nature. This book was released on 2023-04-19 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent developments in multivariate and robust statistical methods. Featuring contributions by leading experts in the field it covers various topics, including multivariate and high-dimensional methods, time series, graphical models, robust estimation, supervised learning and normal extremes. It will appeal to statistics and data science researchers, PhD students and practitioners who are interested in modern multivariate and robust statistics. The book is dedicated to David E. Tyler on the occasion of his pending retirement and also includes a review contribution on the popular Tyler’s shape matrix.

The SAGE Handbook of Multilevel Modeling

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Publisher : SAGE
ISBN 13 : 1473971314
Total Pages : 954 pages
Book Rating : 4.4/5 (739 download)

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Book Synopsis The SAGE Handbook of Multilevel Modeling by : Marc A. Scott

Download or read book The SAGE Handbook of Multilevel Modeling written by Marc A. Scott and published by SAGE. This book was released on 2013-08-31 with total page 954 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling. The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field. Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference. Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models. Part III includes discussion of missing data and robust methods, assessment of fit and software. Part IV consists of exemplary modeling and data analyses written by methodologists working in specific disciplines. Combining practical pieces with overviews of the field, this Handbook is essential reading for any student or researcher looking to apply multilevel techniques in their own research.

Non-Standard Problems in Inference for Additive and Linear Mixed Models

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Publisher : Cuvillier Verlag
ISBN 13 : 3867274916
Total Pages : 153 pages
Book Rating : 4.8/5 (672 download)

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Book Synopsis Non-Standard Problems in Inference for Additive and Linear Mixed Models by : Sonja Greven

Download or read book Non-Standard Problems in Inference for Additive and Linear Mixed Models written by Sonja Greven and published by Cuvillier Verlag. This book was released on 2008 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Generalized Inference for Mixed Linear Models

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ISBN 13 :
Total Pages : 472 pages
Book Rating : 4.:/5 (782 download)

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Book Synopsis Generalized Inference for Mixed Linear Models by : Paul L. Patterson

Download or read book Generalized Inference for Mixed Linear Models written by Paul L. Patterson and published by . This book was released on 2006 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Multilevel Analysis

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Publisher : SAGE
ISBN 13 : 144625433X
Total Pages : 369 pages
Book Rating : 4.4/5 (462 download)

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Book Synopsis Multilevel Analysis by : Tom A B Snijders

Download or read book Multilevel Analysis written by Tom A B Snijders and published by SAGE. This book was released on 2011-10-30 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Second Edition of this classic text introduces the main methods, techniques and issues involved in carrying out multilevel modeling and analysis. Snijders and Bosker′s book is an applied, authoritative and accessible introduction to the topic, providing readers with a clear conceptual and practical understanding of all the main issues involved in designing multilevel studies and conducting multilevel analysis. This book provides step-by-step coverage of: • multilevel theories • ecological fallacies • the hierarchical linear model • testing and model specification • heteroscedasticity • study designs • longitudinal data • multivariate multilevel models • discrete dependent variables There are also new chapters on: • missing data • multilevel modeling and survey weights • Bayesian and MCMC estimation and latent-class models. This book has been comprehensively revised and updated since the last edition, and now discusses modeling using HLM, MLwiN, SAS, Stata including GLLAMM, R, SPSS, Mplus, WinBugs, Latent Gold, and SuperMix. This is a must-have text for any student, teacher or researcher with an interest in conducting or understanding multilevel analysis. Tom A.B. Snijders is Professor of Statistics in the Social Sciences at the University of Oxford and Professor of Statistics and Methodology at the University of Groningen. Roel J. Bosker is Professor of Education and Director of GION, Groningen Institute for Educational Research, at the University of Groningen.

Journal of the American Statistical Association

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Publisher :
ISBN 13 :
Total Pages : 764 pages
Book Rating : 4.:/5 (318 download)

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Book Synopsis Journal of the American Statistical Association by :

Download or read book Journal of the American Statistical Association written by and published by . This book was released on 2007 with total page 764 pages. Available in PDF, EPUB and Kindle. Book excerpt: A scientific and educational journal not only for professional statisticians but also for economists, business executives, research directors, government officials, university professors, and others who are seriously interested in the application of statistical methods to practical problems, in the development of more useful methods, and in the improvement of basic statistical data.

Robust Statistics

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Publisher : John Wiley & Sons
ISBN 13 : 1119214688
Total Pages : 466 pages
Book Rating : 4.1/5 (192 download)

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Book Synopsis Robust Statistics by : Ricardo A. Maronna

Download or read book Robust Statistics written by Ricardo A. Maronna and published by John Wiley & Sons. This book was released on 2019-01-04 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new edition of this popular text on robust statistics, thoroughly updated to include new and improved methods and focus on implementation of methodology using the increasingly popular open-source software R. Classical statistics fail to cope well with outliers associated with deviations from standard distributions. Robust statistical methods take into account these deviations when estimating the parameters of parametric models, thus increasing the reliability of fitted models and associated inference. This new, second edition of Robust Statistics: Theory and Methods (with R) presents a broad coverage of the theory of robust statistics that is integrated with computing methods and applications. Updated to include important new research results of the last decade and focus on the use of the popular software package R, it features in-depth coverage of the key methodology, including regression, multivariate analysis, and time series modeling. The book is illustrated throughout by a range of examples and applications that are supported by a companion website featuring data sets and R code that allow the reader to reproduce the examples given in the book. Unlike other books on the market, Robust Statistics: Theory and Methods (with R) offers the most comprehensive, definitive, and up-to-date treatment of the subject. It features chapters on estimating location and scale; measuring robustness; linear regression with fixed and with random predictors; multivariate analysis; generalized linear models; time series; numerical algorithms; and asymptotic theory of M-estimates. Explains both the use and theoretical justification of robust methods Guides readers in selecting and using the most appropriate robust methods for their problems Features computational algorithms for the core methods Robust statistics research results of the last decade included in this 2nd edition include: fast deterministic robust regression, finite-sample robustness, robust regularized regression, robust location and scatter estimation with missing data, robust estimation with independent outliers in variables, and robust mixed linear models. Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is an ideal resource for researchers, practitioners, and graduate students in statistics, engineering, computer science, and physical and social sciences.

Statistical Inference for Mixed Linear Models with Two Variance Components

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Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (77 download)

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Book Synopsis Statistical Inference for Mixed Linear Models with Two Variance Components by : George Y. Wong

Download or read book Statistical Inference for Mixed Linear Models with Two Variance Components written by George Y. Wong and published by . This book was released on 1978 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Inference for a Class of Mixed Linear Models with Spatial and Temporal Correlations

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ISBN 13 :
Total Pages : 290 pages
Book Rating : 4.:/5 (298 download)

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Book Synopsis Inference for a Class of Mixed Linear Models with Spatial and Temporal Correlations by : Lisa Doyle

Download or read book Inference for a Class of Mixed Linear Models with Spatial and Temporal Correlations written by Lisa Doyle and published by . This book was released on 1993 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt:

A Boosting Approach to High Dimensional Linear Mixed Model

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Publisher :
ISBN 13 :
Total Pages : 73 pages
Book Rating : 4.:/5 (1 download)

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Book Synopsis A Boosting Approach to High Dimensional Linear Mixed Model by : Haoyang Fan

Download or read book A Boosting Approach to High Dimensional Linear Mixed Model written by Haoyang Fan and published by . This book was released on 2015 with total page 73 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the growing need in biomedical and sociology research, longitudinal data are attracting more and more researchers. In these studies, data are usually collected over time across different subjects. Longitudinal and panel data offer us many opportunities to study the correlations between variables and model changes over time. In past decades, linear mixed models have been successfully used to analyze longitudinal and panel data. Linear mixed model can not only describe the data trajectory but also model the dependence across subjects. However, with the advent of modern technology, modeling, inference and interpretation of linear mixed models are greatly challenged by the high dimension of fixed and random effects in many longitudinal and panel data. In this dissertation, I propose a new boosting estimate to fit high dimensional linear mixed model. The theoretical properties of the boosting approach are well studied. Under mild conditions, asymptotic consistency of fixed effects estimate in our boosting framework does not depend on random effects estimate, structures and underlying distributions in high dimensional linear mixed model. The asymptotic consistency is also guaranteed when the dimension of covariates for both fixed and random effects diverging to infinity as sample size increasing. Simulations and real data analysis are also provided to demonstrate advantages of proposed boosting approach comparing to some existing methods.

Linear and Generalized Linear Mixed Models and Their Applications

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Publisher : Springer Science & Business Media
ISBN 13 : 0387479465
Total Pages : 269 pages
Book Rating : 4.3/5 (874 download)

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Book Synopsis Linear and Generalized Linear Mixed Models and Their Applications by : Jiming Jiang

Download or read book Linear and Generalized Linear Mixed Models and Their Applications written by Jiming Jiang and published by Springer Science & Business Media. This book was released on 2007-05-30 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. It presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. The book offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it includes recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models. The book is aimed at students, researchers and other practitioners who are interested in using mixed models for statistical data analysis.

On the Inference of Random Effects in Generalized Linear Mixed Models

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Publisher :
ISBN 13 :
Total Pages : 76 pages
Book Rating : 4.:/5 (14 download)

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Book Synopsis On the Inference of Random Effects in Generalized Linear Mixed Models by : Daniel Antonio Flores Agreda

Download or read book On the Inference of Random Effects in Generalized Linear Mixed Models written by Daniel Antonio Flores Agreda and published by . This book was released on 2017 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: