Analysis of Variance for Functional Data, Zhang, Jin-Ting
Автор: Shi, Jian Qing Название: Gaussian Process Regression Analysis for Functional Data ISBN: 1439837732 ISBN-13(EAN): 9781439837733 Издательство: Taylor&Francis Рейтинг: Цена: 24499.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Fr?d?ric Ferraty Название: Recent Advances in Functional Data Analysis and Related Topics ISBN: 3790828335 ISBN-13(EAN): 9783790828337 Издательство: Springer Рейтинг: Цена: 25853.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The failure of standard multivariate statistics to analyze such functional data has led the statistical community to develop appropriate statistical methodologies, called Functional Data Analysis (FDA).
Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling data with an emphasis on how to incorporate specific ideas (hypotheses) about the structure of the data into a linear model for the data. The book carefully analyzes small data sets by using tools that are easily scaled to big data. The tools also apply to small relevant data sets that are extracted from big data.
New to the Second Edition
Reorganized to focus on unbalanced data
Reworked balanced analyses using methods for unbalanced data
Introductions to nonparametric and lasso regression
Introductions to general additive and generalized additive models
Examination of homologous factors
Unbalanced split plot analyses
Extensions to generalized linear models
R, Minitab(R), and SAS code on the author's website
The text can be used in a variety of courses, including a yearlong graduate course on regression and ANOVA or a data analysis course for upper-division statistics students and graduate students from other fields. It places a strong emphasis on interpreting the range of computer output encountered when dealing with unbalanced data.
Автор: Davies, Patrick Laurie Название: Data Analysis and Approximate Models ISBN: 1482215861 ISBN-13(EAN): 9781482215861 Издательство: Taylor&Francis Рейтинг: Цена: 24499.00 р. Наличие на складе: Нет в наличии.
Автор: Hocking Ronald R Название: Methods and Applications of Linear Models ISBN: 1118329503 ISBN-13(EAN): 9781118329504 Издательство: Wiley Рейтинг: Цена: 20109.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Praise for the Second Edition "An essential desktop reference book... it should definitely be on your bookshelf.
Автор: Lawal Bayo, Famoye Felix Название: Applied Statistics: Regression and Analysis of Variance ISBN: 0761861718 ISBN-13(EAN): 9780761861713 Издательство: Неизвестно Рейтинг: Цена: 24827.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents a thorough treatment of the methods of regression and analysis of variance. Applied Statistics requires an understanding of introductory statistics courses and is suitable for both junior and senior undergraduate students.
Автор: Cox, D.R. Название: Components of Variance ISBN: 1584883545 ISBN-13(EAN): 9781584883548 Издательство: Taylor&Francis Рейтинг: Цена: 17609.00 р. Наличие на складе: Нет в наличии.
Автор: Cox, D.r. Solomon, P.j. Название: Components of variance ISBN: 0367395975 ISBN-13(EAN): 9780367395971 Издательство: Taylor&Francis Рейтинг: Цена: 10104.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Identifying the sources and measuring the impact of haphazard variations are important in any number of research applications, from clinical trials and genetics to industrial design and psychometric testing. Only in very simple situations can such variations be represented effectively by independent, identically distributed random variables or by random sampling from a hypothetical infinite population. Components of Variance illuminates the complexities of the subject, setting forth its principles with focus on both the development of models for detailed analyses and the statistical techniques themselves. The authors first consider balanced and unbalanced situations, then move to the treatment of non-normal data, beginning with the Poisson and binomial models and followed by extensions to survival data and more general situations. In the final chapter, they discuss ways of extending and assessing various models, including the study of exceedances, the use of nonlinear representations, the study of transformations of the response variable, and the detailed examination of the distributional form of the underlying random variables. Careful signposting and numerous examples from genetic data analysis, clinical trial design, longitudinal data analysis, industrial design, and meta-analysis make this book accessible - and valuable - not only to statisticians but to all applied research scientists who use statistical methods.
Автор: Shaked, Moshe Название: Stable Non-Gaussian Random Processes ISBN: 0412051710 ISBN-13(EAN): 9780412051715 Издательство: Taylor&Francis Рейтинг: Цена: 29093.00 р. Наличие на складе: Нет в наличии.
Автор: Alonso-gutierrez, David Bastero, Jesus Название: Approaching the kannan-lovasz-simonovits and variance conjectures ISBN: 3319132628 ISBN-13(EAN): 9783319132624 Издательство: Springer Рейтинг: Цена: 4890.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Focusing on two central conjectures of Asymptotic Geometric Analysis, the Kannan-Lovasz-Simonovits spectral gap conjecture and the variance conjecture, these Lecture Notes present the theory in an accessible way, so that interested readers, even those who are not experts in the field, will be able to appreciate the treated topics.
Автор: Srivastava Название: Functional and Shape Data Analysis ISBN: 149394018X ISBN-13(EAN): 9781493940189 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This textbook for courses on function data analysis and shape data analysis describes how to define, compare, and mathematically represent shapes, with a focus on statistical modeling and inference. It is aimed at graduate students in analysis in statistics, engineering, applied mathematics, neuroscience, biology, bioinformatics, and other related areas. The interdisciplinary nature of the broad range of ideas covered—from introductory theory to algorithmic implementations and some statistical case studies—is meant to familiarize graduate students with an array of tools that are relevant in developing computational solutions for shape and related analyses. These tools, gleaned from geometry, algebra, statistics, and computational science, are traditionally scattered across different courses, departments, and disciplines; Functional and Shape Data Analysis offers a unified, comprehensive solution by integrating the registration problem into shape analysis, better preparing graduate students for handling future scientific challenges.Recently, a data-driven and application-oriented focus on shape analysis has been trending. This text offers a self-contained treatment of this new generation of methods in shape analysis of curves. Its main focus is shape analysis of functions and curves—in one, two, and higher dimensions—both closed and open. It develops elegant Riemannian frameworks that provide both quantification of shape differences and registration of curves at the same time. Additionally, these methods are used for statistically summarizing given curve data, performing dimension reduction, and modeling observed variability. It is recommended that the reader have a background in calculus, linear algebra, numerical analysis, and computation.
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