Mathematik und Technologie

Author: Christiane Rousseau
Publisher: Springer-Verlag
ISBN: 3642300928
Format: PDF, Docs
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Zusammen mit der Abstraktion ist die Mathematik das entscheidende Werkzeug für technologische Innovationen. Das Buch bietet eine Einführung in zahlreiche Anwendungen der Mathematik auf dem Gebiet der Technologie. Meist werden moderne Anwendungen dargestellt, die heute zum Alltag gehören. Die mathematischen Grundlagen für technologische Anwendungen sind dabei relativ elementar, was die Leistungsstärke der mathematischen Modellbildung und der mathematischen Hilfsmittel beweist. Mit zahlreichen originellen Übungen am Ende eines jeden Kapitels.

Linear Mixed Models for Longitudinal Data

Author: Geert Verbeke
Publisher: Springer Science & Business Media
ISBN: 1441902996
Format: PDF, ePub, Docs
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This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place. Most analyses were done with the MIXED procedure of the SAS software package, but the data analyses are presented in a software-independent fashion.

Models for Discrete Longitudinal Data

Author: Geert Molenberghs
Publisher: Springer Science & Business Media
ISBN: 0387289801
Format: PDF, Docs
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The linear mixed model has become the main parametric tool for the analysis of continuous longitudinal data, as the authors discussed in their 2000 book. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The authors received the American Statistical Association's Excellence in Continuing Education Award based on short courses on longitudinal and incomplete data at the Joint Statistical Meetings of 2002 and 2004.

Dynamic Mixed Models for Familial Longitudinal Data

Author: Brajendra C. Sutradhar
Publisher: Springer Science & Business Media
ISBN: 9781441983428
Format: PDF, Docs
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This book provides a theoretical foundation for the analysis of discrete data such as count and binary data in the longitudinal setup. Unlike the existing books, this book uses a class of auto-correlation structures to model the longitudinal correlations for the repeated discrete data that accommodates all possible Gaussian type auto-correlation models as special cases including the equi-correlation models. This new dynamic modelling approach is utilized to develop theoretically sound inference techniques such as the generalized quasi-likelihood (GQL) technique for consistent and efficient estimation of the underlying regression effects involved in the model, whereas the existing ‘working’ correlations based GEE (generalized estimating equations) approach has serious theoretical limitations both for consistent and efficient estimation, and the existing random effects based correlations approach is not suitable to model the longitudinal correlations. The book has exploited the random effects carefully only to model the correlations of the familial data. Subsequently, this book has modelled the correlations of the longitudinal data collected from the members of a large number of independent families by using the class of auto-correlation structures conditional on the random effects. The book also provides models and inferences for discrete longitudinal data in the adaptive clinical trial set up. The book is mathematically rigorous and provides details for the development of estimation approaches under selected familial and longitudinal models. Further, while the book provides special cares for mathematics behind the correlation models, it also presents the illustrations of the statistical analysis of various real life data. This book will be of interest to the researchers including graduate students in biostatistics and econometrics, among other applied statistics research areas. Brajendra Sutradhar is a University Research Professor at Memorial University in St. John’s, Canada. He is an elected member of the International Statistical Institute and a fellow of the American Statistical Association. He has published about 110 papers in statistics journals in the area of multivariate analysis, time series analysis including forecasting, sampling, survival analysis for correlated failure times, robust inferences in generalized linear mixed models with outliers, and generalized linear longitudinal mixed models with bio-statistical and econometric applications. He has served as an associate editor for six years for Canadian Journal of Statistics and for four years for the Journal of Environmental and Ecological Statistics. He has served for 3 years as a member of the advisory committee on statistical methods in Statistics Canada. Professor Sutradhar was awarded 2007 distinguished service award of Statistics Society of Canada for his many years of services to the society including his special services for society’s annual meetings.

Mixed Effects Regression Models in Linguistics

Author: Dirk Speelman
Publisher: Springer
ISBN: 3319698303
Format: PDF, ePub, Mobi
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When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addresses a number of common complications, misunderstandings, and pitfalls. Topics that are covered include the use of huge datasets, dealing with non-linear relations, issues of cross-validation, and issues of model selection and complex random structures. The volume features examples from various subfields in linguistics. The book also provides R code for a wide range of analyses.

Bilinear Regression Analysis

Author: Dietrich von Rosen
Publisher: Springer
ISBN: 3319787845
Format: PDF, ePub
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This book expands on the classical statistical multivariate analysis theory by focusing on bilinear regression models, a class of models comprising the classical growth curve model and its extensions. In order to analyze the bilinear regression models in an interpretable way, concepts from linear models are extended and applied to tensor spaces. Further, the book considers decompositions of tensor products into natural subspaces, and addresses maximum likelihood estimation, residual analysis, influential observation analysis and testing hypotheses, where properties of estimators such as moments, asymptotic distributions or approximations of distributions are also studied. Throughout the text, examples and several analyzed data sets illustrate the different approaches, and fresh insights into classical multivariate analysis are provided. This monograph is of interest to researchers and Ph.D. students in mathematical statistics, signal processing and other fields where statistical multivariate analysis is utilized. It can also be used as a text for second graduate-level courses on multivariate analysis.

Correlated Data Analysis Modeling Analytics and Applications

Author: Peter X. -K. Song
Publisher: Springer Science & Business Media
ISBN: 038771393X
Format: PDF
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This book covers recent developments in correlated data analysis. It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to cover a broader range of data types than the traditional generalized linear models. The reader is provided with a systematic treatment for the topic of estimating functions, and both generalized estimating equations (GEE) and quadratic inference functions (QIF) are studied as special cases. In addition to the discussions on marginal models and mixed-effects models, this book covers new topics on joint regression analysis based on Gaussian copulas.

Applied Mixed Models in Medicine

Author: Helen Brown
Publisher: John Wiley & Sons
ISBN: 1118778243
Format: PDF, ePub, Mobi
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A fully updated edition of this key text on mixed models,focusing on applications in medical research The application of mixed models is an increasingly popular wayof analysing medical data, particularly in the pharmaceuticalindustry. A mixed model allows the incorporation of both fixed andrandom variables within a statistical analysis, enabling efficientinferences and more information to be gained from the data. Therehave been many recent advances in mixed modelling, particularlyregarding the software and applications. This third edition ofBrown and Prescott’s groundbreaking text provides an updateon the latest developments, and includes guidance on the use ofcurrent SAS techniques across a wide range of applications. Presents an overview of the theory and applications of mixedmodels in medical research, including the latest developments andnew sections on incomplete block designs and the analysis ofbilateral data. Easily accessible to practitioners in any area where mixedmodels are used, including medical statisticians andeconomists. Includes numerous examples using real data from medical andhealth research, and epidemiology, illustrated with SAS code andoutput. Features the new version of SAS, including new graphics formodel diagnostics and the procedure PROC MCMC. Supported by a website featuring computer code, data sets, andfurther material. This third edition will appeal to applied statisticians workingin medical research and the pharmaceutical industry, as well asteachers and students of statistics courses in mixed models. Thebook will also be of great value to a broad range of scientists,particularly those working in the medical and pharmaceuticalareas.

Intervention Research

Author: Bernadette Mazurek Melnyk, PhD, RN, CPNP/ PMHNP, FNAP, FAAN
Publisher: Springer Publishing Company
ISBN: 0826109586
Format: PDF, ePub, Docs
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2012 First Place AJN Book of the Year Award Winner in Nursing Research! "This is a resource for success and should be a part of any researcher's library."--Doody's Medical Reviews This book is a practical, user-friendly guide for health care researchers across multiple disciplines who are involved in intervention research. It provides all of the essential elements needed for understanding how to design, conduct, analyze, and fund intervention studies that are replicable and can withstand the scrutiny of the Institutional Review Board and peer review. Developed from an annual continuing education workshop on intervention studies conducted by Dr. Melnyk, this text is the most comprehensive body of information available on this topic. Contributors address the design of interventions that are ethically considerate and sensitive to culture, race/ethnicity, and gender, minimizing threats to external and internal validity, measurement, and budgeting. The guide explores such implementation issues as subject recruitment and retention, data management, and specialized settings, cost analysis, and explaining intervention effects. The text also guides readers in writing grant applications that fund , and addresses how to move intervention study findings into the real world. A unique addition to the book is the availability of digital examples of progress reports, final reports, and research grant applications that have received funding from the National Institutes of Health and other relevant organizations. This text is a valuable resource for all health care professionals conducting research and for doctoral students in health care studies. Key Features: Presents the essential tools for designing, conducting, analyzing, and funding intervention studies Designed for use by health care professionals conducting intervention research Provides comprehensive, accessible guidelines for doctoral students across all health care disciplines Instructs readers on writing grant applications that fund Includes digital examples of funded research grants, progress reports, and final reports

Nichtparametrische Analyse longitudinaler Daten

Author: Edgar Brunner
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3486798944
Format: PDF
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Das Buch richtet sich sowohl an Statistiker und Biometriker in der Praxis als auch an die Anwender, die sich mit den Auswertungsverfahren ihrer Daten und der näheren Beschreibung dieser Methoden beschäftigen möchten. Die Verfahren und Modelle werden anhand zahlreicher Beispiele aus Medizin, Pharmakologie und Forstwissenschaft dargestellt. Aus dem Inhalt: Modelle. Effekte und Hypothesen. Schätzer für die relativen Effekte. Teststatistiken. Software. Versuchsanlagen für eine Gruppe. Versuchsanlagen für mehrere Gruppen. Abhängige Meßwiederholungen. Mehrfaktorielle Versuchsanlagen. Zahlreiche Meßzeitpunkte. Ausblick und offene Probleme. Orginaldaten. Ergebnisse der SAS-Makros.