Smoothing Methods In Statistics

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Smoothing Methods in Statistics

Author : Jeffrey S. Simonoff
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

Author : Wolfgang Härdle
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

Author : Rafael A. Irizarry
Publisher : CRC Press
ISBN 13 : 1000708039
Total Pages : 794 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 794 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.

Applied Smoothing Techniques for Data Analysis

Author : Adrian W. Bowman
Publisher : OUP Oxford
ISBN 13 : 0191545694
Total Pages : 205 pages
Book Rating : 4.1/5 (915 download)

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Book Synopsis Applied Smoothing Techniques for Data Analysis by : Adrian W. Bowman

Download or read book Applied Smoothing Techniques for Data Analysis written by Adrian W. Bowman and published by OUP Oxford. This book was released on 1997-08-14 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book describes the use of smoothing techniques in statistics, including both density estimation and nonparametric regression. Considerable advances in research in this area have been made in recent years. The aim of this text is to describe a variety of ways in which these methods can be applied to practical problems in statistics. The role of smoothing techniques in exploring data graphically is emphasised, but the use of nonparametric curves in drawing conclusions from data, as an extension of more standard parametric models, is also a major focus of the book. Examples are drawn from a wide range of applications. The book is intended for those who seek an introduction to the area, with an emphasis on applications rather than on detailed theory. It is therefore expected that the book will benefit those attending courses at an advanced undergraduate, or postgraduate, level, as well as researchers, both from statistics and from other disciplines, who wish to learn about and apply these techniques in practical data analysis. The text makes extensive reference to S-Plus, as a computing environment in which examples can be explored. S-Plus functions and example scripts are provided to implement many of the techniques described. These parts are, however, clearly separate from the main body of text, and can therefore easily be skipped by readers not interested in S-Plus.

Smoothing Spline ANOVA Models

Author : Chong Gu
Publisher : Springer Science & Business Media
ISBN 13 : 1461453690
Total Pages : 446 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Smoothing Spline ANOVA Models by : Chong Gu

Download or read book Smoothing Spline ANOVA Models written by Chong Gu and published by Springer Science & Business Media. This book was released on 2013-01-26 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonparametric function estimation with stochastic data, otherwise known as smoothing, has been studied by several generations of statisticians. Assisted by the ample computing power in today's servers, desktops, and laptops, smoothing methods have been finding their ways into everyday data analysis by practitioners. While scores of methods have proved successful for univariate smoothing, ones practical in multivariate settings number far less. Smoothing spline ANOVA models are a versatile family of smoothing methods derived through roughness penalties, that are suitable for both univariate and multivariate problems. In this book, the author presents a treatise on penalty smoothing under a unified framework. Methods are developed for (i) regression with Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under a variety of sampling schemes; and (iii) hazard rate estimation with censored life time data and covariates. The unifying themes are the general penalized likelihood method and the construction of multivariate models with built-in ANOVA decompositions. Extensive discussions are devoted to model construction, smoothing parameter selection, computation, and asymptotic convergence. Most of the computational and data analytical tools discussed in the book are implemented in R, an open-source platform for statistical computing and graphics. Suites of functions are embodied in the R package gss, and are illustrated throughout the book using simulated and real data examples. This monograph will be useful as a reference work for researchers in theoretical and applied statistics as well as for those in other related disciplines. It can also be used as a text for graduate level courses on the subject. Most of the materials are accessible to a second year graduate student with a good training in calculus and linear algebra and working knowledge in basic statistical inferences such as linear models and maximum likelihood estimates.

Smoothing of Multivariate Data

Author : Jussi Sakari Klemelä
Publisher : John Wiley & Sons
ISBN 13 : 0470425660
Total Pages : 641 pages
Book Rating : 4.4/5 (74 download)

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Book Synopsis Smoothing of Multivariate Data by : Jussi Sakari Klemelä

Download or read book Smoothing of Multivariate Data written by Jussi Sakari Klemelä and published by John Wiley & Sons. This book was released on 2009-09-04 with total page 641 pages. Available in PDF, EPUB and Kindle. Book excerpt: An applied treatment of the key methods and state-of-the-art tools for visualizing and understanding statistical data Smoothing of Multivariate Data provides an illustrative and hands-on approach to the multivariate aspects of density estimation, emphasizing the use of visualization tools. Rather than outlining the theoretical concepts of classification and regression, this book focuses on the procedures for estimating a multivariate distribution via smoothing. The author first provides an introduction to various visualization tools that can be used to construct representations of multivariate functions, sets, data, and scales of multivariate density estimates. Next, readers are presented with an extensive review of the basic mathematical tools that are needed to asymptotically analyze the behavior of multivariate density estimators, with coverage of density classes, lower bounds, empirical processes, and manipulation of density estimates. The book concludes with an extensive toolbox of multivariate density estimators, including anisotropic kernel estimators, minimization estimators, multivariate adaptive histograms, and wavelet estimators. A completely interactive experience is encouraged, as all examples and figurescan be easily replicated using the R software package, and every chapter concludes with numerous exercises that allow readers to test their understanding of the presented techniques. The R software is freely available on the book's related Web site along with "Code" sections for each chapter that provide short instructions for working in the R environment. Combining mathematical analysis with practical implementations, Smoothing of Multivariate Data is an excellent book for courses in multivariate analysis, data analysis, and nonparametric statistics at the upper-undergraduate and graduatelevels. It also serves as a valuable reference for practitioners and researchers in the fields of statistics, computer science, economics, and engineering.

Forecasting with Exponential Smoothing

Author : Rob Hyndman
Publisher : Springer Science & Business Media
ISBN 13 : 3540719180
Total Pages : 362 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Forecasting with Exponential Smoothing by : Rob Hyndman

Download or read book Forecasting with Exponential Smoothing written by Rob Hyndman and published by Springer Science & Business Media. This book was released on 2008-06-19 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exponential smoothing methods have been around since the 1950s, and are still the most popular forecasting methods used in business and industry. However, a modeling framework incorporating stochastic models, likelihood calculation, prediction intervals and procedures for model selection, was not developed until recently. This book brings together all of the important new results on the state space framework for exponential smoothing. It will be of interest to people wanting to apply the methods in their own area of interest as well as for researchers wanting to take the ideas in new directions. Part 1 provides an introduction to exponential smoothing and the underlying models. The essential details are given in Part 2, which also provide links to the most important papers in the literature. More advanced topics are covered in Part 3, including the mathematical properties of the models and extensions of the models for specific problems. Applications to particular domains are discussed in Part 4.

Kernel Smoothing

Author : M.P. Wand
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

Forecasting: principles and practice

Author : Rob J Hyndman
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.

Bayesian Filtering and Smoothing

Author : Simo Särkkä
Publisher : Cambridge University Press
ISBN 13 : 110703065X
Total Pages : 255 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Bayesian Filtering and Smoothing by : Simo Särkkä

Download or read book Bayesian Filtering and Smoothing written by Simo Särkkä and published by Cambridge University Press. This book was released on 2013-09-05 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.

Smoothing Splines

Author : Yuedong Wang
Publisher : CRC Press
ISBN 13 : 1420077562
Total Pages : 380 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Smoothing Splines by : Yuedong Wang

Download or read book Smoothing Splines written by Yuedong Wang and published by CRC Press. This book was released on 2011-06-22 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: A general class of powerful and flexible modeling techniques, spline smoothing has attracted a great deal of research attention in recent years and has been widely used in many application areas, from medicine to economics. Smoothing Splines: Methods and Applications covers basic smoothing spline models, including polynomial, periodic, spherical, t

Smoothing and Regression

Author : Michael G. Schimek
Publisher : John Wiley & Sons
ISBN 13 : 1118763300
Total Pages : 682 pages
Book Rating : 4.1/5 (187 download)

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Book Synopsis Smoothing and Regression by : Michael G. Schimek

Download or read book Smoothing and Regression written by Michael G. Schimek and published by John Wiley & Sons. This book was released on 2013-05-29 with total page 682 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to a wide variety of univariate and multivariate smoothing techniques for regression Smoothing and Regression: Approaches, Computation, and Application bridges the many gaps that exist among competing univariate and multivariate smoothing techniques. It introduces, describes, and in some cases compares a large number of the latest and most advanced techniques for regression modeling. Unlike many other volumes on this topic, which are highly technical and specialized, this book discusses all methods in light of both computational efficiency and their applicability for real data analysis. Using examples of applications from the biosciences, environmental sciences, engineering, and economics, as well as medical research and marketing, this volume addresses the theory, computation, and application of each approach. A number of the techniques discussed, such as smoothing under shape restrictions or of dependent data, are presented for the first time in book form. Special features of this book include: * Comprehensive coverage of smoothing and regression with software hints and applications from a wide variety of disciplines * A unified, easy-to-follow format * Contributions from more than 25 leading researchers from around the world * More than 150 illustrations also covering new graphical techniques important for exploratory data analysis and visualization of high-dimensional problems * Extensive end-of-chapter references For professionals and aspiring professionals in statistics, applied mathematics, computer science, and econometrics, as well as for researchers in the applied and social sciences, Smoothing and Regression is a unique and important new resource destined to become one the most frequently consulted references in the field.

Multivariate Kernel Smoothing and Its Applications

Author : José E. Chacón
Publisher : CRC Press
ISBN 13 : 0429939132
Total Pages : 327 pages
Book Rating : 4.4/5 (299 download)

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Book Synopsis Multivariate Kernel Smoothing and Its Applications by : José E. Chacón

Download or read book Multivariate Kernel Smoothing and Its Applications written by José E. Chacón and published by CRC Press. This book was released on 2018-05-08 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kernel smoothing has greatly evolved since its inception to become an essential methodology in the data science tool kit for the 21st century. Its widespread adoption is due to its fundamental role for multivariate exploratory data analysis, as well as the crucial role it plays in composite solutions to complex data challenges. Multivariate Kernel Smoothing and Its Applications offers a comprehensive overview of both aspects. It begins with a thorough exposition of the approaches to achieve the two basic goals of estimating probability density functions and their derivatives. The focus then turns to the applications of these approaches to more complex data analysis goals, many with a geometric/topological flavour, such as level set estimation, clustering (unsupervised learning), principal curves, and feature significance. Other topics, while not direct applications of density (derivative) estimation but sharing many commonalities with the previous settings, include classification (supervised learning), nearest neighbour estimation, and deconvolution for data observed with error. For a data scientist, each chapter contains illustrative Open data examples that are analysed by the most appropriate kernel smoothing method. The emphasis is always placed on an intuitive understanding of the data provided by the accompanying statistical visualisations. For a reader wishing to investigate further the details of their underlying statistical reasoning, a graduated exposition to a unified theoretical framework is provided. The algorithms for efficient software implementation are also discussed. José E. Chacón is an associate professor at the Department of Mathematics of the Universidad de Extremadura in Spain. Tarn Duong is a Senior Data Scientist for a start-up which provides short distance carpooling services in France. Both authors have made important contributions to kernel smoothing research over the last couple of decades.

Kernel Smoothing

Author : Sucharita Ghosh
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.

Statistical Methods for Forecasting

Author : Bovas Abraham
Publisher : John Wiley & Sons
ISBN 13 : 0470317299
Total Pages : 474 pages
Book Rating : 4.4/5 (73 download)

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Book Synopsis Statistical Methods for Forecasting by : Bovas Abraham

Download or read book Statistical Methods for Forecasting written by Bovas Abraham and published by John Wiley & Sons. This book was released on 2009-09-25 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "This book, it must be said, lives up to the words on its advertising cover: 'Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical, intermediate level discussion of a variety of forecasting tools, and explains how they relate to one another, both in theory and practice.' It does just that!" -Journal of the Royal Statistical Society "A well-written work that deals with statistical methods and models that can be used to produce short-term forecasts, this book has wide-ranging applications. It could be used in the context of a study of regression, forecasting, and time series analysis by PhD students; or to support a concentration in quantitative methods for MBA students; or as a work in applied statistics for advanced undergraduates." -Choice Statistical Methods for Forecasting is a comprehensive, readable treatment of statistical methods and models used to produce short-term forecasts. The interconnections between the forecasting models and methods are thoroughly explained, and the gap between theory and practice is successfully bridged. Special topics are discussed, such as transfer function modeling; Kalman filtering; state space models; Bayesian forecasting; and methods for forecast evaluation, comparison, and control. The book provides time series, autocorrelation, and partial autocorrelation plots, as well as examples and exercises using real data. Statistical Methods for Forecasting serves as an outstanding textbook for advanced undergraduate and graduate courses in statistics, business, engineering, and the social sciences, as well as a working reference for professionals in business, industry, and government.

Statistical Theory and Computational Aspects of Smoothing

Author : Wolfgang Härdle
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.

Nonparametric Methods in Statistics with SAS Applications

Author : Olga Korosteleva
Publisher : CRC Press
ISBN 13 : 1466580631
Total Pages : 193 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Nonparametric Methods in Statistics with SAS Applications by : Olga Korosteleva

Download or read book Nonparametric Methods in Statistics with SAS Applications written by Olga Korosteleva and published by CRC Press. This book was released on 2013-08-19 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designed for a graduate course in applied statistics, Nonparametric Methods in Statistics with SAS Applications teaches students how to apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event analysis, density estimation, and resampling methods.The text begins wit