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Robust Mixed Model Analysis, Jiang Jiming



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Автор: Jiang Jiming
Название:  Robust Mixed Model Analysis
ISBN: 9789814733830
Издательство: World Scientific Publishing
Классификация:
ISBN-10: 9814733830
Обложка/Формат: Hardcover
Страницы: 268
Вес: 0.53 кг.
Дата издания: 08.05.2019
Серия: Mathematics
Язык: English
Размер: 229 x 152 x 16
Читательская аудитория: Postgraduate, research & scholarly
Ключевые слова: Probability & statistics, MATHEMATICS / Probability & Statistics / General,MATHEMATICS / Applied,SCIENCE / Research & Methodology
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Поставляется из: Англии



Introduction to Probability, Second Edition

Автор: Joseph K. Blitzstein, Jessica Hwang
Название: Introduction to Probability, Second Edition
ISBN: 1138369918 ISBN-13(EAN): 9781138369917
Издательство: Taylor&Francis
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Цена: 10888 р.
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Описание: Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory.? The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces.The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment. The second edition adds many new examples, exercises, and explanations, to deepen understanding of the ideas, clarify subtle concepts, and respond to feedback from many students and readers. New supplementary online resources have been developed, including animations and interactive visualizations, and the book has been updated to dovetail with these resources.? Supplementary material is available on Joseph Blitzstein’s website www. stat110.net. The supplements include:Solutions to selected exercisesAdditional practice problemsHandouts including review material and sample exams Animations and interactive visualizations created in connection with the edX online version of Stat 110.Links to lecture videos available on ITunes U and YouTube There is also a complete instructor's solutions manual available to instructors who require the book for a course.

Statistical Analysis with Missing Data, Third Edit ion

Автор: Little
Название: Statistical Analysis with Missing Data, Third Edit ion
ISBN: 0470526793 ISBN-13(EAN): 9780470526798
Издательство: Wiley
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Цена: 13703 р.
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Описание:

AN UP-TO-DATE, COMPREHENSIVE TREATMENT OF A CLASSIC TEXT ON MISSING DATA IN STATISTICS

The topic of missing data has gained considerable attention in recent decades. This new edition by two acknowledged experts on the subject offers an up-to-date account of practical methodology for handling missing data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism, and then they apply the theory to a wide range of important missing data problems.

Statistical Analysis with Missing Data, Third Edition starts by introducing readers to the subject and approaches toward solving it. It looks at the patterns and mechanisms that create the missing data, as well as a taxonomy of missing data. It then goes on to examine missing data in experiments, before discussing complete-case and available-case analysis, including weighting methods. The new edition expands its coverage to include recent work on topics such as nonresponse in sample surveys, causal inference, diagnostic methods, and sensitivity analysis, among a host of other topics.

  • An updated "classic" written by renowned authorities on the subject
  • Features over 150 exercises (including many new ones)
  • Covers recent work on important methods like multiple imputation, robust alternatives to weighting, and Bayesian methods
  • Revises previous topics based on past student feedback and class experience
  • Contains an updated and expanded bibliography

Statistical Analysis with Missing Data, Third Edition is an ideal textbook for upper undergraduate and/or beginning graduate level students of the subject. It is also an excellent source of information for applied statisticians and practitioners in government and industry.

Time Series Analysis

Автор: Hamilton, James
Название: Time Series Analysis
ISBN: 0691042896 ISBN-13(EAN): 9780691042893
Издательство: Wiley
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Цена: 11798 р.
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Описание: A graduate-level text which describes the recent dramatic changes that have taken place in the way that researchers analyze economic and financial time series. It explores such important innovations as vector regression, nonlinear time series models and the generalized methods of moments.

Applied Mixed Model Analysis: A Practical Guide

Автор: Jos W. R. Twisk
Название: Applied Mixed Model Analysis: A Practical Guide
ISBN: 110872776X ISBN-13(EAN): 9781108727761
Издательство: Cambridge Academ
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Цена: 8133 р.
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Описание: This practical book is designed for applied researchers who want to use mixed models with their data. It discusses the basic principles of mixed model analysis, including two-level and three-level structures, and covers continuous outcome variables, dichotomous outcome variables, and categorical and survival outcome variables. Emphasizing interpretation of results, the book develops the most important applications of mixed models, such as the study of group differences, longitudinal data analysis, multivariate mixed model analysis, IPD meta-analysis, and mixed model predictions. All examples are analyzed with STATA, and an extensive overview and comparison of alternative software packages is provided. All datasets used in the book are available for download, so readers can re-analyze the examples to gain a strong understanding of the methods. Although most examples are taken from epidemiological and clinical studies, this book is also highly recommended for researchers working in other fields.

Applied Mixed Model Analysis: A Practical Guide

Автор: Jos W. R. Twisk
Название: Applied Mixed Model Analysis: A Practical Guide
ISBN: 1108480578 ISBN-13(EAN): 9781108480574
Издательство: Cambridge Academ
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Цена: 18076 р.
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Описание: This practical book is designed for applied researchers who want to use mixed models with their data. It discusses the basic principles of mixed model analysis, including two-level and three-level structures, and covers continuous outcome variables, dichotomous outcome variables, and categorical and survival outcome variables. Emphasizing interpretation of results, the book develops the most important applications of mixed models, such as the study of group differences, longitudinal data analysis, multivariate mixed model analysis, IPD meta-analysis, and mixed model predictions. All examples are analyzed with STATA, and an extensive overview and comparison of alternative software packages is provided. All datasets used in the book are available for download, so readers can re-analyze the examples to gain a strong understanding of the methods. Although most examples are taken from epidemiological and clinical studies, this book is also highly recommended for researchers working in other fields.

Robust Multivariate Analysis

Автор: David Olive
Название: Robust Multivariate Analysis
ISBN: 3319682512 ISBN-13(EAN): 9783319682518
Издательство: Springer
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Цена: 11136 р.
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Описание: This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. The robust techniques are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis.

Robust Technology with Analysis of Interference in Signal Processing

Автор: Aliev Telman
Название: Robust Technology with Analysis of Interference in Signal Processing
ISBN: 0306474794 ISBN-13(EAN): 9780306474798
Издательство: Springer
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Цена: 20789 р.
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Описание: Robust Technology with Analysis of Interference in Signal Processing discusses for the first time the theoretical fundamentals and algorithms of analysis of noise as an information carrier. On their basis the robust technology of noisy signals processing is developed. This technology can be applied to solving the problems of control, identification, diagnostics, and pattern recognition in petrochemistry, energetics, geophysics, medicine, physics, aviation, and other sciences and industries. The text explores the emergent possibility of forecasting failures on various objects, in conjunction with the fact that failures follow the hidden microchanges revealed via interference estimates. This monograph is of interest to students, postgraduates, engineers, scientific associates and others who are concerned with the processing of measuring information on computers.

Understanding Robust and Exploratory Data Analysis

Автор: David C. Hoaglin
Название: Understanding Robust and Exploratory Data Analysis
ISBN: 0471384917 ISBN-13(EAN): 9780471384915
Издательство: Wiley
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Цена: 22688 р.
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Описание: Originally published in hardcover in 1982, this book is now offered in a Wiley Classics Library edition. A contributed volume, edited by some of the preeminent statisticians of the 20th century, Understanding of Robust and Exploratory Data Analysis explains why and how to use exploratory data analysis and robust and resistant methods in statistical practice.

Robust Diagnostic Regression Analysis

Автор: Atkinson
Название: Robust Diagnostic Regression Analysis
ISBN: 0387950176 ISBN-13(EAN): 9780387950174
Издательство: Springer
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Цена: 24501 р.
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Описание: This book is about using graphs to understand the relationship between a regression model and the data to which it is fitted. Because of the new way in which models are fitted, for example by least squares, we can lose information about the effect of individual observations on inferences about the form and parameters of the model. The methods developed in this book reveal how the fitted regression model depends on individual observations and on groups of observations.

Robust procedures can sometimes reveal this structure, but downweight or discard some observations. The novelty in this book is to combine robustness and a "forward" search through the data with regression diagnostics and computer graphics.

Methodology in Robust and Nonparametric Statistics

Автор: Jurekova
Название: Methodology in Robust and Nonparametric Statistics
ISBN: 1439840687 ISBN-13(EAN): 9781439840689
Издательство: Taylor&Francis
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Цена: 25410 р.
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Описание: Robust and nonparametric statistical methods have their foundation in fields ranging from agricultural science to astronomy, from biomedical sciences to the public health disciplines, and, more recently, in genomics, bioinformatics, and financial statistics. These disciplines are presently nourished by data mining and high-level computer-based algorithms, but to work actively with robust and nonparametric procedures, practitioners need to understand their background. Explaining the underpinnings of robust methods and recent theoretical developments, Methodology in Robust and Nonparametric Statistics provides a profound mathematically rigorous explanation of the methodology of robust and nonparametric statistical procedures. Thoroughly up-to-date, this book Presents multivariate robust and nonparametric estimation with special emphasis on affine-equivariant procedures, followed by hypotheses testing and confidence sets Keeps mathematical abstractions at bay while remaining largely theoretical Provides a pool of basic mathematical tools used throughout the book in derivations of main results The methodology presented, with due emphasis on asymptotics and interrelations, will pave the way for further developments on robust statistical procedures in more complex models. Using examples to illustrate the methods, the text highlights applications in the fields of biomedical science, bioinformatics, finance, and engineering. In addition, the authors provide exercises in the text.

Robust Cluster Analysis and Variable Selection

Автор: Ritter
Название: Robust Cluster Analysis and Variable Selection
ISBN: 1439857962 ISBN-13(EAN): 9781439857960
Издательство: Taylor&Francis
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Цена: 25410 р.
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Описание: Clustering remains a vibrant area of research in statistics. Although there are many books on this topic, there are relatively few that are well founded in the theoretical aspects. In Robust Cluster Analysis and Variable Selection, Gunter Ritter presents an overview of the theory and applications of probabilistic clustering and variable selection, synthesizing the key research results of the last 50 years. The author focuses on the robust clustering methods he found to be the most useful on simulated data and real-time applications. The book provides clear guidance for the varying needs of both applications, describing scenarios in which accuracy and speed are the primary goals. Robust Cluster Analysis and Variable Selection includes all of the important theoretical details, and covers the key probabilistic models, robustness issues, optimization algorithms, validation techniques, and variable selection methods. The book illustrates the different methods with simulated data and applies them to real-world data sets that can be easily downloaded from the web. This provides you with guidance in how to use clustering methods as well as applicable procedures and algorithms without having to understand their probabilistic fundamentals.

Автор: Jure Kova
Название: Robust Statistical Methods With R,
ISBN: 113803536X ISBN-13(EAN): 9781138035362
Издательство: Taylor&Francis
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Цена: 16333 р.
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Описание: Robust statistical methods are particularly useful for obtaining good results from non-normally distributed data with outliers. This book is the second edition of a book focussed on applying robust statistical methods using R. It is a substantial update, with new chapters on multivariate models, large sample versus finite sample behavior, and measurement error models. The most significant update is that the computing component has been greatly improved with R code and examples now integrated throughout the book. The book is supplemented by a website with all code and data available.


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