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Local Regression And Likelihood
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Book Synopsis Local Regression and Likelihood by : Clive Loader
Download or read book Local Regression and Likelihood written by Clive Loader and published by Springer Science & Business Media. This book was released on 2006-05-09 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Separation of signal from noise is the most fundamental problem in data analysis, arising in such fields as: signal processing, econometrics, actuarial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, with extensions to local likelihood and density estimation. Practical information is also included on how to implement these methods in the programs S-PLUS and LOCFIT.
Book Synopsis Local Polynomial Kernel Regression for Generalized Linear Models and Quasi-likelihood Functions by : Jianqing Fan
Download or read book Local Polynomial Kernel Regression for Generalized Linear Models and Quasi-likelihood Functions written by Jianqing Fan and published by . This book was released on 1992 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Penalized Likelihood Estimation by : Paul P. Eggermont
Download or read book Maximum Penalized Likelihood Estimation written by Paul P. Eggermont and published by Springer Science & Business Media. This book was released on 2009-06-02 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unique blend of asymptotic theory and small sample practice through simulation experiments and data analysis. Novel reproducing kernel Hilbert space methods for the analysis of smoothing splines and local polynomials. Leading to uniform error bounds and honest confidence bands for the mean function using smoothing splines Exhaustive exposition of algorithms, including the Kalman filter, for the computation of smoothing splines of arbitrary order.
Book Synopsis Local Polynomial Modelling and Its Applications by : Jianqing Fan
Download or read book Local Polynomial Modelling and Its Applications written by Jianqing Fan and published by Routledge. This book was released on 2018-05-02 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-analytic approaches to regression problems, arising from many scientific disciplines are described in this book. The aim of these nonparametric methods is to relax assumptions on the form of a regression function and to let data search for a suitable function that describes the data well. The use of these nonparametric functions with parametric techniques can yield very powerful data analysis tools. Local polynomial modeling and its applications provides an up-to-date picture on state-of-the-art nonparametric regression techniques. The emphasis of the book is on methodologies rather than on theory, with a particular focus on applications of nonparametric techniques to various statistical problems. High-dimensional data-analytic tools are presented, and the book includes a variety of examples. This will be a valuable reference for research and applied statisticians, and will serve as a textbook for graduate students and others interested in nonparametric regression.
Book Synopsis Local Fitting of Regression Models by Likelihood by : Nils Lid Hjort
Download or read book Local Fitting of Regression Models by Likelihood written by Nils Lid Hjort and published by . This book was released on 1994 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Theory and Computational Aspects of Smoothing by : Wolfgang Härdle
Download or read book Statistical Theory and Computational Aspects of Smoothing written by Wolfgang Härdle and published by Springer Science & Business Media. This book was released on 2013-03-08 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the main applications of statistical smoothing techniques is nonparametric regression. For the last 15 years there has been a strong theoretical interest in the development of such techniques. Related algorithmic concepts have been a main concern in computational statistics. Smoothing techniques in regression as well as other statistical methods are increasingly applied in biosciences and economics. But they are also relevant for medical and psychological research. Introduced are new developments in scatterplot smoothing and applications in statistical modelling. The treatment of the topics is on an intermediate level avoiding too much technicalities. Computational and applied aspects are considered throughout. Of particular interest to readers is the discussion of recent local fitting techniques.
Book Synopsis Local Polynomial Modelling and Its Applications by : Jianqing Fan
Download or read book Local Polynomial Modelling and Its Applications written by Jianqing Fan and published by Routledge. This book was released on 2018-05-02 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-analytic approaches to regression problems, arising from many scientific disciplines are described in this book. The aim of these nonparametric methods is to relax assumptions on the form of a regression function and to let data search for a suitable function that describes the data well. The use of these nonparametric functions with parametric techniques can yield very powerful data analysis tools. Local polynomial modeling and its applications provides an up-to-date picture on state-of-the-art nonparametric regression techniques. The emphasis of the book is on methodologies rather than on theory, with a particular focus on applications of nonparametric techniques to various statistical problems. High-dimensional data-analytic tools are presented, and the book includes a variety of examples. This will be a valuable reference for research and applied statisticians, and will serve as a textbook for graduate students and others interested in nonparametric regression.
Book Synopsis A Simple Local Least Squares Approach for Estimating the Regression Function of Binary Response Data and Related Data-driven Bandwidth Selection Procedures by : Aaron Kenji Aragaki
Download or read book A Simple Local Least Squares Approach for Estimating the Regression Function of Binary Response Data and Related Data-driven Bandwidth Selection Procedures written by Aaron Kenji Aragaki and published by . This book was released on 1995 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Local Likelihood and Local Partial Likelihood in Hazard Regression by : Martin S. King
Download or read book Local Likelihood and Local Partial Likelihood in Hazard Regression written by Martin S. King and published by . This book was released on 1997 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis On Maximum Likelihood Estimation for Two-phase Linear Regression by : David Luther Sylwester
Download or read book On Maximum Likelihood Estimation for Two-phase Linear Regression written by David Luther Sylwester and published by . This book was released on 1965 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Local Likelihood for Non-Parametric Arch(1) Models by : Francesco Audrino
Download or read book Local Likelihood for Non-Parametric Arch(1) Models written by Francesco Audrino and published by . This book was released on 2005 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a local likelihood estimation for the log-transformed ARCH(1) model in the financial field. Our nonparametric estimator is constructed within the likelihood framework for non-Gaussian observations: It is different from standard kernel regression smoothing, where the innovations are assumed to be normally distributed. We derive consistency and asymptotic normality for our estimators and conclude from simulation and real data analysis that the local likelihood estimator has better predictive potential than classical local regression.
Book Synopsis Semantic Computing by : Phillip Chen-yu Sheu
Download or read book Semantic Computing written by Phillip Chen-yu Sheu and published by World Scientific Publishing Company. This book was released on 2017-08-23 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the first volume of World Scientific Encyclopedia with Semantic Computing and Robotic Intelligence, this volume is designed to lay the foundation for the understanding of the Semantic Computing (SC), as a core concept to study Robotic Intelligence in the subsequent volumes.This volume aims to provide a reference to the development of Semantic Computing, in the terms of 'meaning', 'context', and 'intention'. It brings together a series of technical notes, in average, no longer than 10 pages in length, each focuses on one topic in Semantic Computing; being review article or research paper, to explain the fundamental concepts, models or algorithms, and possible applications of the technology concerned.This volume will address three core areas in Semantic Computing:
Book Synopsis Data Mining and Business Analytics with R by : Johannes Ledolter
Download or read book Data Mining and Business Analytics with R written by Johannes Ledolter and published by John Wiley & Sons. This book was released on 2013-05-28 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools Illustrations of how to use the outlined concepts in real-world situations Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.
Book Synopsis Handbook of Computational Statistics by : James E. Gentle
Download or read book Handbook of Computational Statistics written by James E. Gentle and published by Springer Science & Business Media. This book was released on 2012-07-06 with total page 1180 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Computational Statistics - Concepts and Methods (second edition) is a revision of the first edition published in 2004, and contains additional comments and updated information on the existing chapters, as well as three new chapters addressing recent work in the field of computational statistics. This new edition is divided into 4 parts in the same way as the first edition. It begins with "How Computational Statistics became the backbone of modern data science" (Ch.1): an overview of the field of Computational Statistics, how it emerged as a separate discipline, and how its own development mirrored that of hardware and software, including a discussion of current active research. The second part (Chs. 2 - 15) presents several topics in the supporting field of statistical computing. Emphasis is placed on the need for fast and accurate numerical algorithms, and some of the basic methodologies for transformation, database handling, high-dimensional data and graphics treatment are discussed. The third part (Chs. 16 - 33) focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data. Lastly, a set of selected applications (Chs. 34 - 38) like Bioinformatics, Medical Imaging, Finance, Econometrics and Network Intrusion Detection highlight the usefulness of computational statistics in real-world applications.
Book Synopsis Observed Brain Dynamics by : Partha Mitra
Download or read book Observed Brain Dynamics written by Partha Mitra and published by Oxford University Press. This book was released on 2007-12-07 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: The biomedical sciences have recently undergone revolutionary change, due to the ability to digitize and store large data sets. In neuroscience, the data sources include measurements of neural activity measured using electrode arrays, EEG and MEG, brain imaging data from PET, fMRI, and optical imaging methods. Analysis, visualization, and management of these time series data sets is a growing field of research that has become increasingly important both for experimentalists and theorists interested in brain function. Written by investigators who have played an important role in developing the subject and in its pedagogical exposition, the current volume addresses the need for a textbook in this interdisciplinary area. The book is written for a broad spectrum of readers ranging from physical scientists, mathematicians, and statisticians wishing to educate themselves about neuroscience, to biologists who would like to learn time series analysis methods in particular and refresh their mathematical and statistical knowledge in general, through self-pedagogy. It may also be used as a supplement for a quantitative course in neurobiology or as a textbook for instruction on neural signal processing. The first part of the book contains a set of essays meant to provide conceptual background which are not technical and shall be generally accessible. Salient features include the adoption of an active perspective of the nervous system, an emphasis on function, and a brief survey of different theoretical accounts in neuroscience. The second part is the longest in the book, and contains a refresher course in mathematics and statistics leading up to time series analysis techniques. The third part contains applications of data analysis techniques to the range of data sources indicated above (also available as part of the Chronux data analysis platform from http://chronux.org), and the fourth part contains special topics.
Book Synopsis Encyclopedia of Environmetrics by : Abdel H. El-Shaarawi
Download or read book Encyclopedia of Environmetrics written by Abdel H. El-Shaarawi and published by John Wiley & Sons. This book was released on 2002 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive overview of environmetric research and its applications... Environmetrics covers the development and application of quantitative methods in the environmental sciences. It provides essential tools for understanding, predicting, and controlling the impacts of agents, both man-made and natural, which affect the environment. Basic and applied research in this area covers a broad range of topics. Primary among these are the quantitative sciences, such as statistics, probability and applied mathematics, chemometrics, and econometrics. Applications are also important, for example in, ecology and environmental biology, public health, atmospheric science, geology, engineering, risk management, and regulatory/governmental policy amongst others. * Divided into 12 sections, the Encyclopedia brings together over 600 detailed articles which have been carefully selected and reviewed through the collaborative efforts of the Editors-in-Chief and the appropriate Section Editor * Presented in alphabetical order all the articles will include an explanatory introduction, extensive cross-referencing and an up-to-date bibliography providing literature references for further reading. Presenting state of the art information in a readable, highly accessible style, the scope and coverage provided by the Encyclopedia of Environmetrics will ensure its place as the landmark reference for the many scientists, educators, and decision-makers working across this multidisciplinary field. An essential reference tool for university libraries, research laboratories, government institutions and consultancies concerned with the environmental sciences, the Encyclopedia of Environmetrics brings together for the first time, comprehensive coverage of the full range of topics, techniques and applications covered by this multidisciplinary field. There is currently no central reference source which addresses the needs of this multidisciplinary community. This new Encyclopedia will fill this gap by providing a comprehensive source of relevant fundamental concepts in environmetric research, development and applications for statisticians, mathematicians, economists, environmentalists, ecologist, government officials and policy makers.
Book Synopsis Smoothing Methods in Statistics by : Jeffrey S. Simonoff
Download or read book Smoothing Methods in Statistics written by Jeffrey S. Simonoff and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focussing on applications, this book covers a very broad range, including simple and complex univariate and multivariate density estimation, nonparametric regression estimation, categorical data smoothing, and applications of smoothing to other areas of statistics. It will thus be of particular interest to data analysts, as arguments generally proceed from actual data rather than statistical theory, while the "Background Material" sections will interest statisticians studying the field. Over 750 references allow researchers to find the original sources for more details, and the "Computational Issues" sections provide sources for statistical software that use the methods discussed. Each chapter includes exercises with a heavily computational focus based upon the data sets used in the book, making it equally suitable as a textbook for a course in smoothing.
