Local Approximation Techniques in Signal and Image Processing PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Local Approximation Techniques in Signal and Image Processing PDF full book. Access full book title Local Approximation Techniques in Signal and Image Processing by Vladimir I︠A︡kovlevich Katkovnik. Download full books in PDF and EPUB format.
Author: Vladimir I︠A︡kovlevich Katkovnik Publisher: SPIE-International Society for Optical Engineering ISBN: Category : Computers Languages : en Pages : 584
Book Description
This book deals with a wide class of novel and efficient adaptive signal processing techniques developed to restore signals from noisy and degraded observations. These signals include those acquired from still or video cameras, electron microscopes, radar, X-rays, or ultrasound devices, and are used for various purposes, including entertainment, medical, business, industrial, military, civil, security, and scientific. In many cases useful information and high quality must be extracted from the imaging. However, often raw signals are not directly suitable for this purpose and must be processed in some way. Such processing is called signal reconstruction. This book is devoted to a recent and original approach to signal reconstruction based on combining two independent ideas: local polynomial approximation and the intersection of confidence interval rule.
Author: Vladimir I︠A︡kovlevich Katkovnik Publisher: SPIE-International Society for Optical Engineering ISBN: Category : Computers Languages : en Pages : 584
Book Description
This book deals with a wide class of novel and efficient adaptive signal processing techniques developed to restore signals from noisy and degraded observations. These signals include those acquired from still or video cameras, electron microscopes, radar, X-rays, or ultrasound devices, and are used for various purposes, including entertainment, medical, business, industrial, military, civil, security, and scientific. In many cases useful information and high quality must be extracted from the imaging. However, often raw signals are not directly suitable for this purpose and must be processed in some way. Such processing is called signal reconstruction. This book is devoted to a recent and original approach to signal reconstruction based on combining two independent ideas: local polynomial approximation and the intersection of confidence interval rule.
Author: Heungsun Hwang Publisher: CRC Press ISBN: 146659294X Category : Mathematics Languages : en Pages : 346
Book Description
Developed by the authors, generalized structured component analysis is an alternative to two longstanding approaches to structural equation modeling: covariance structure analysis and partial least squares path modeling. Generalized structured component analysis allows researchers to evaluate the adequacy of a model as a whole, compare a model to alternative specifications, and conduct complex analyses in a straightforward manner. Generalized Structured Component Analysis: A Component-Based Approach to Structural Equation Modeling provides a detailed account of this novel statistical methodology and its various extensions. The authors present the theoretical underpinnings of generalized structured component analysis and demonstrate how it can be applied to various empirical examples. The book enables quantitative methodologists, applied researchers, and practitioners to grasp the basic concepts behind this new approach and apply it to their own research. The book emphasizes conceptual discussions throughout while relegating more technical intricacies to the chapter appendices. Most chapters compare generalized structured component analysis to partial least squares path modeling to show how the two component-based approaches differ when addressing an identical issue. The authors also offer a free, online software program (GeSCA) and an Excel-based software program (XLSTAT) for implementing the basic features of generalized structured component analysis.
Author: Paul D. McNicholas Publisher: CRC Press ISBN: 1482225670 Category : Mathematics Languages : en Pages : 212
Book Description
"This is a great overview of the field of model-based clustering and classification by one of its leading developers. McNicholas provides a resource that I am certain will be used by researchers in statistics and related disciplines for quite some time. The discussion of mixtures with heavy tails and asymmetric distributions will place this text as the authoritative, modern reference in the mixture modeling literature." (Douglas Steinley, University of Missouri) Mixture Model-Based Classification is the first monograph devoted to mixture model-based approaches to clustering and classification. This is both a book for established researchers and newcomers to the field. A history of mixture models as a tool for classification is provided and Gaussian mixtures are considered extensively, including mixtures of factor analyzers and other approaches for high-dimensional data. Non-Gaussian mixtures are considered, from mixtures with components that parameterize skewness and/or concentration, right up to mixtures of multiple scaled distributions. Several other important topics are considered, including mixture approaches for clustering and classification of longitudinal data as well as discussion about how to define a cluster Paul D. McNicholas is the Canada Research Chair in Computational Statistics at McMaster University, where he is a Professor in the Department of Mathematics and Statistics. His research focuses on the use of mixture model-based approaches for classification, with particular attention to clustering applications, and he has published extensively within the field. He is an associate editor for several journals and has served as a guest editor for a number of special issues on mixture models.
Author: DONALD B. MACNAUGHTON Publisher: ISBN: Category : Languages : en Pages : 260
Book Description
From the preface The "threshold p-value"-the arbiter of statistical significance-has been a widely used gateway to believability and acceptance for publication in scientific research since 1925. However, a growing number of statisticians and other researchers say we should "move beyond" these ideas, suggesting we should greatly reduce our emphasis on them in scientific research. These authors are waging a well-intentioned, polite, and vigorous intellectual war on the ideas of a threshold p-value and statistical significance. This is a "good" war, because it forces important issues into the open, where they can be best understood and assessed. This book grew from a sense that the threshold-p-value gateway to publication of scientific research results is highly useful but is also widely misunderstood. The book presents, from first principles, a modern view of the role of the gateway, as used by some scientific journals. The ideas are explained in terms of the recent disagreement about them between the editorial in a Special Issue on Statistical Inference of the American Statistician and a subsequent editorial in the New England Journal of Medicine. The ideas are developed with almost no reference to mathematics. (A computer can do all the standard math if the user properly understands the key ideas.) The explanations are reinforced with practical examples. The discussion shows how the concept of a threshold-p-value gateway helps researchers and journal editors maximize the overall scientific, social, and commercial benefit of scientific research. The gateway does this by optimally balancing the rates of costly "false-positive" and "false-negative" errors in a scientific journal. The book also discusses the important related ideas of a relationship between variables, a scientific hypothesis test, and the "replication crisis" in some branches of scientific research. The body of the book, which covers the key ideas, is roughly 30% of the text. The remainder consists of 23 appendices that expand the ideas in useful directions. The material is aimed at scientific researchers, journal editors, science teachers, and science students in the biological, social, and physical sciences. It will also be of interest to statisticians, data scientists, philosophers of science, and lay readers seeking an integrated modern view of the high-level operation of the study of relationships between variables in scientific research. About the author Donald B. Macnaughton has been a statistical consultant for more than 40 years. He has managed the statistical aspects of research in the fields of experimental psychology, zoology, drug dependence, nursing, education, business, geography, physical education, and inmate rehabilitation, among others. His consulting work supports and informs his main interest, which is to read, understand, and write about the vital role of the field of statistics in scientific research.
Author: Art B. Owen Publisher: CRC Press ISBN: 1420036157 Category : Mathematics Languages : en Pages : 322
Book Description
Empirical likelihood provides inferences whose validity does not depend on specifying a parametric model for the data. Because it uses a likelihood, the method has certain inherent advantages over resampling methods: it uses the data to determine the shape of the confidence regions, and it makes it easy to combined data from multiple sources. It al
Author: Vladimir I︠A︡kovlevich Katkovnik
Author: Vladimir I︠A︡kovlevich Katkovnik
Author: Malcolm Ray McNeil
Author: Heungsun Hwang
Author: Paul D. McNicholas
Author: DONALD B. MACNAUGHTON
Author: Art B. Owen