Описание: The collection and analysis of data play an important role in many fields of science and technology, such as computational biology, quantitative finance, information engineering, machine learning, neuroscience, medicine, and the social sciences.
Описание: The contents of the papers evolve around multivariate analysis and random matrices with topics such as high-dimensional analysis, goodness-of-fit measures, variable selection and information criteria, inference of covariance structures, the Wishart distribution and growth curve models.
Описание: This book presents modern methods and real-world applications of compositional data analysis.
Автор: Gregory R. Hancock, Jeffrey R. Harring, George B. Macready Название: Advances in Latent Class Analysis: A Festschrift in Honor of C. Mitchell Dayton ISBN: 164113562X ISBN-13(EAN): 9781641135627 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 14276.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: What is latent class analysis? If you asked that question thirty or forty years ago you would have gotten a different answer than you would today. Closer to its time of inception, latent class analysis was viewed primarily as a categorical data analysis technique, often framed as a factor analysis model where both the measured variable indicators and underlying latent variables are categorical. Today, however, it rests within much broader mixture and diagnostic modeling framework, integrating measured and latent variables that may be categorical and/or continuous, and where latent classes serve to define the subpopulations for whom many aspects of the focal measured and latent variable model may differ. For latent class analysis to take these developmental leaps required contributions that were methodological, certainly, as well as didactic. Among the leaders on both fronts was C. Mitchell “Chan” Dayton, at the University of Maryland, whose work in latent class analysis spanning several decades helped the method to expand and reach its current potential. The current volume in the Center for Integrated Latent Variable Research (CILVR) series reflects the diversity that is latent class analysis today, celebrating work related to, made possible by, and inspired by Chan’s noted contributions, and signaling the even more exciting future yet to come.
Автор: Gregory R. Hancock, Jeffrey R. Harring, George B. Macready Название: Advances in Latent Class Analysis: A Festschrift in Honor of C. Mitchell Dayton ISBN: 1641135611 ISBN-13(EAN): 9781641135610 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 7623.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: What is latent class analysis? If you asked that question thirty or forty years ago you would have gotten a different answer than you would today. Closer to its time of inception, latent class analysis was viewed primarily as a categorical data analysis technique, often framed as a factor analysis model where both the measured variable indicators and underlying latent variables are categorical. Today, however, it rests within much broader mixture and diagnostic modeling framework, integrating measured and latent variables that may be categorical and/or continuous, and where latent classes serve to define the subpopulations for whom many aspects of the focal measured and latent variable model may differ. For latent class analysis to take these developmental leaps required contributions that were methodological, certainly, as well as didactic. Among the leaders on both fronts was C. Mitchell “Chan” Dayton, at the University of Maryland, whose work in latent class analysis spanning several decades helped the method to expand and reach its current potential. The current volume in the Center for Integrated Latent Variable Research (CILVR) series reflects the diversity that is latent class analysis today, celebrating work related to, made possible by, and inspired by Chan’s noted contributions, and signaling the even more exciting future yet to come.
Описание: This volume contains 11 papers on optimum designs for linear and non-linear models and groups of papers on designs for quality improvement, designs in agriculture and for model building. Non-design problems include robustness in linear models and estimation problems in non-linear models.
Автор: Sandeep Sen, Amit Kumar Название: Design and Analysis of Algorithms: A Contemporary Perspective ISBN: 1108496822 ISBN-13(EAN): 9781108496827 Издательство: Cambridge Academ Рейтинг: Цена: 7445.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A valuable text in the field of computer science and engineering, covering fundamental concepts and recent advancements. To help the reader to design/redesign algorithms for their requirements rather than be overawed by the challenges of a new framework.
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