Smoothing Techniques for Curve Estimation

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Publisher : Springer
ISBN 13 : 3540384758
Total Pages : 254 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Smoothing Techniques for Curve Estimation by : T. Gasser

Download or read book Smoothing Techniques for Curve Estimation written by T. Gasser and published by Springer. This book was released on 2006-12-08 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Smoothing Techniques for Curve Estimation

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Publisher :
ISBN 13 : 9783662181867
Total Pages : 262 pages
Book Rating : 4.1/5 (818 download)

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Book Synopsis Smoothing Techniques for Curve Estimation by : Thomas Gasser

Download or read book Smoothing Techniques for Curve Estimation written by Thomas Gasser and published by . This book was released on 2014-01-15 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Smoothing Techniques

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Author :
Publisher : Springer Science & Business Media
ISBN 13 : 1461244323
Total Pages : 267 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Smoothing Techniques by : Wolfgang Härdle

Download or read book Smoothing Techniques written by Wolfgang Härdle and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: The author has attempted to present a book that provides a non-technical introduction into the area of non-parametric density and regression function estimation. The application of these methods is discussed in terms of the S computing environment. Smoothing in high dimensions faces the problem of data sparseness. A principal feature of smoothing, the averaging of data points in a prescribed neighborhood, is not really practicable in dimensions greater than three if we have just one hundred data points. Additive models provide a way out of this dilemma; but, for their interactiveness and recursiveness, they require highly effective algorithms. For this purpose, the method of WARPing (Weighted Averaging using Rounded Points) is described in great detail.

Introduction to Data Science

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Publisher : CRC Press
ISBN 13 : 1000708039
Total Pages : 836 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Introduction to Data Science by : Rafael A. Irizarry

Download or read book Introduction to Data Science written by Rafael A. Irizarry and published by CRC Press. This book was released on 2019-11-20 with total page 836 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

Kernel Smoothing

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Publisher : John Wiley & Sons
ISBN 13 : 111845605X
Total Pages : 272 pages
Book Rating : 4.1/5 (184 download)

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Book Synopsis Kernel Smoothing by : Sucharita Ghosh

Download or read book Kernel Smoothing written by Sucharita Ghosh and published by John Wiley & Sons. This book was released on 2018-01-09 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive theoretical overview of kernel smoothing methods with motivating examples Kernel smoothing is a flexible nonparametric curve estimation method that is applicable when parametric descriptions of the data are not sufficiently adequate. This book explores theory and methods of kernel smoothing in a variety of contexts, considering independent and correlated data e.g. with short-memory and long-memory correlations, as well as non-Gaussian data that are transformations of latent Gaussian processes. These types of data occur in many fields of research, e.g. the natural and the environmental sciences, and others. Nonparametric density estimation, nonparametric and semiparametric regression, trend and surface estimation in particular for time series and spatial data and other topics such as rapid change points, robustness etc. are introduced alongside a study of their theoretical properties and optimality issues, such as consistency and bandwidth selection. Addressing a variety of topics, Kernel Smoothing: Principles, Methods and Applications offers a user-friendly presentation of the mathematical content so that the reader can directly implement the formulas using any appropriate software. The overall aim of the book is to describe the methods and their theoretical backgrounds, while maintaining an analytically simple approach and including motivating examples—making it extremely useful in many sciences such as geophysics, climate research, forestry, ecology, and other natural and life sciences, as well as in finance, sociology, and engineering. A simple and analytical description of kernel smoothing methods in various contexts Presents the basics as well as new developments Includes simulated and real data examples Kernel Smoothing: Principles, Methods and Applications is a textbook for senior undergraduate and graduate students in statistics, as well as a reference book for applied statisticians and advanced researchers.

Nonparametric Functional Estimation and Related Topics

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Publisher : Springer Science & Business Media
ISBN 13 : 9780792312260
Total Pages : 732 pages
Book Rating : 4.3/5 (122 download)

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Book Synopsis Nonparametric Functional Estimation and Related Topics by : George Roussas

Download or read book Nonparametric Functional Estimation and Related Topics written by George Roussas and published by Springer Science & Business Media. This book was released on 1991-04-30 with total page 732 pages. Available in PDF, EPUB and Kindle. Book excerpt: About three years ago, an idea was discussed among some colleagues in the Division of Statistics at the University of California, Davis, as to the possibility of holding an international conference, focusing exclusively on nonparametric curve estimation. The fruition of this idea came about with the enthusiastic support of this project by Luc Devroye of McGill University, Canada, and Peter Robinson of the London School of Economics, UK. The response of colleagues, contacted to ascertain interest in participation in such a conference, was gratifying and made the effort involved worthwhile. Devroye and Robinson, together with this editor and George Metakides of the University of Patras, Greece and of the European Economic Communities, Brussels, formed the International Organizing Committee for a two week long Advanced Study Institute (ASI) sponsored by the Scientific Affairs Division of the North Atlantic Treaty Organization (NATO). The ASI was held on the Greek Island of Spetses between July 29 and August 10, 1990. Nonparametric functional estimation is a central topic in statistics, with applications in numerous substantive fields in mathematics, natural and social sciences, engineering and medicine. While there has been interest in nonparametric functional estimation for many years, this has grown of late, owing to increasing availability of large data sets and the ability to process them by means of improved computing facilities, along with the ability to display the results by means of sophisticated graphical procedures.

Nonparametric Curve Estimation from Time Series

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Publisher : Springer
ISBN 13 : 146123686X
Total Pages : 157 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Nonparametric Curve Estimation from Time Series by : Lazlo Györfi

Download or read book Nonparametric Curve Estimation from Time Series written by Lazlo Györfi and published by Springer. This book was released on 2013-12-21 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: Because of the sheer size and scope of the plastics industry, the title Developments in Plastics Technology now covers an incredibly wide range of subjects or topics. No single volume can survey the whole field in any depth and what follows is, therefore, a series of chapters on selected topics. The topics were selected by us, the editors, because of their immediate relevance to the plastics industry. When one considers the advancements of the plastics processing machinery (in terms of its speed of operation and conciseness of control), it was felt that several chapters should be included which related to the types of control systems used and the correct usage of hydraulics. The importance of using cellular, rubber-modified and engineering-type plastics has had a major impact on the plastics industry and therefore a chapter on each of these subjects has been included. The two remaining chapters are on the characterisation and behaviour of polymer structures, both subjects again being of current academic or industrial interest. Each of the contributions was written by a specialist in that field and to them all, we, the editors, extend our heartfelt thanks, as writing a contribution for a book such as this, while doing a full-time job, is no easy task.

Forecasting: principles and practice

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Publisher : OTexts
ISBN 13 : 0987507117
Total Pages : 380 pages
Book Rating : 4.9/5 (875 download)

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Book Synopsis Forecasting: principles and practice by : Rob J Hyndman

Download or read book Forecasting: principles and practice written by Rob J Hyndman and published by OTexts. This book was released on 2018-05-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.

Kernel Smoothing

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Publisher : CRC Press
ISBN 13 : 1482216124
Total Pages : 227 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Kernel Smoothing by : M.P. Wand

Download or read book Kernel Smoothing written by M.P. Wand and published by CRC Press. This book was released on 1994-12-01 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. The basic principle is that local averaging or smoothing is performed with respect to a kernel function. This book provides uninitiated readers with a feeling for the principles, applications, and analysis of kernel smoothers. This is facilita

Nonparametric Regression and Spline Smoothing, Second Edition

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Publisher : CRC Press
ISBN 13 : 9780824793371
Total Pages : 368 pages
Book Rating : 4.7/5 (933 download)

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Book Synopsis Nonparametric Regression and Spline Smoothing, Second Edition by : Randall L. Eubank

Download or read book Nonparametric Regression and Spline Smoothing, Second Edition written by Randall L. Eubank and published by CRC Press. This book was released on 1999-02-09 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions of boundary corrections for trigonometric series estimators; detailed asymptotics for polynomial regression; testing goodness-of-fit; estimation in partially linear models; practical aspects, problems and methods for confidence intervals and bands; local polynomial regression; and form and asymptotic properties of linear smoothing splines.

Applied Nonparametric Regression

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Publisher : Cambridge University Press
ISBN 13 : 9780521429504
Total Pages : 356 pages
Book Rating : 4.4/5 (295 download)

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Book Synopsis Applied Nonparametric Regression by : Wolfgang Härdle

Download or read book Applied Nonparametric Regression written by Wolfgang Härdle and published by Cambridge University Press. This book was released on 1990 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable.

Kernel Smoothing in MATLAB

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Author :
Publisher : World Scientific
ISBN 13 : 9814405485
Total Pages : 242 pages
Book Rating : 4.8/5 (144 download)

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Book Synopsis Kernel Smoothing in MATLAB by : Ivana Horová

Download or read book Kernel Smoothing in MATLAB written by Ivana Horová and published by World Scientific. This book was released on 2012 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary: Offers a comprehensive overview of statistical theory and emphases the implementation of presented methods in Matlab. This title contains various Matlab scripts useful for kernel smoothing of density, cumulative distribution function, regression function, hazard function, indices of quality and bivariate density.

Statistical Theory and Computational Aspects of Smoothing

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Publisher : Springer Science & Business Media
ISBN 13 : 3642484255
Total Pages : 265 pages
Book Rating : 4.6/5 (424 download)

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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.

Smoothing Methods in Statistics

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Publisher : Springer Science & Business Media
ISBN 13 : 1461240263
Total Pages : 349 pages
Book Rating : 4.4/5 (612 download)

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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.

Smoothing Techniques for Curve Estimation

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Publisher : Springer Verlag
ISBN 13 : 9783540097068
Total Pages : 245 pages
Book Rating : 4.0/5 (97 download)

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Book Synopsis Smoothing Techniques for Curve Estimation by : T. Gasser

Download or read book Smoothing Techniques for Curve Estimation written by T. Gasser and published by Springer Verlag. This book was released on 1979-11-01 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Image Processing and Jump Regression Analysis

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Publisher : John Wiley & Sons
ISBN 13 : 0471733164
Total Pages : 344 pages
Book Rating : 4.4/5 (717 download)

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Book Synopsis Image Processing and Jump Regression Analysis by : Peihua Qiu

Download or read book Image Processing and Jump Regression Analysis written by Peihua Qiu and published by John Wiley & Sons. This book was released on 2005-05-20 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first text to bridge the gap between image processing andjump regression analysis Recent statistical tools developed to estimate jump curves andsurfaces have broad applications, specifically in the area of imageprocessing. Often, significant differences in technicalterminologies make communication between the disciplines of imageprocessing and jump regression analysis difficult. Ineasy-to-understand language, Image Processing and JumpRegression Analysis builds a bridge between the worlds ofcomputer graphics and statistics by addressing both the connectionsand the differences between these two disciplines. The authorprovides a systematic analysis of the methodology behindnonparametric jump regression analysis by outlining procedures thatare easy to use, simple to compute, and have proven statisticaltheory behind them. Key topics include: Conventional smoothing procedures Estimation of jump regression curves Estimation of jump location curves of regression surfaces Jump-preserving surface reconstruction based on localsmoothing Edge detection in image processing Edge-preserving image restoration With mathematical proofs kept to a minimum, this book isuniquely accessible to a broad readership. It may be used as aprimary text in nonparametric regression analysis and imageprocessing as well as a reference guide for academicians andindustry professionals focused on image processing or curve/surfaceestimation.

Semiparametric Regression

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Publisher : Cambridge University Press
ISBN 13 : 9780521785167
Total Pages : 410 pages
Book Rating : 4.7/5 (851 download)

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Book Synopsis Semiparametric Regression by : David Ruppert

Download or read book Semiparametric Regression written by David Ruppert and published by Cambridge University Press. This book was released on 2003-07-14 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Semiparametric regression is concerned with the flexible incorporation of non-linear functional relationships in regression analyses. Any application area that benefits from regression analysis can also benefit from semiparametric regression. Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular fashion. The authors make liberal use of graphics and examples plus case studies taken from environmental, financial, and other applications. They include practical advice on implementation and pointers to relevant software. The 2003 book is suitable as a textbook for students with little background in regression as well as a reference book for statistically oriented scientists such as biostatisticians, econometricians, quantitative social scientists, epidemiologists, with a good working knowledge of regression and the desire to begin using more flexible semiparametric models. Even experts on semiparametric regression should find something new here.