Density Estimation For Statistics And Data Analysis

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Density Estimation for Statistics and Data Analysis

Bernard. W. Silverman
Density Estimation for Statistics and Data Analysis Book PDF

Author : Bernard. W. Silverman
Publisher : Routledge
Page : 176 pages
File Size : 40,8 Mb
Release : 2018-02-19
Category : Mathematics
ISBN : 9781351456173

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Density Estimation for Statistics and Data Analysis by Bernard. W. Silverman Pdf

Although there has been a surge of interest in density estimation in recent years, much of the published research has been concerned with purely technical matters with insufficient emphasis given to the technique's practical value. Furthermore, the subject has been rather inaccessible to the general statistician. The account presented in this book places emphasis on topics of methodological importance, in the hope that this will facilitate broader practical application of density estimation and also encourage research into relevant theoretical work. The book also provides an introduction to the subject for those with general interests in statistics. The important role of density estimation as a graphical technique is reflected by the inclusion of more than 50 graphs and figures throughout the text. Several contexts in which density estimation can be used are discussed, including the exploration and presentation of data, nonparametric discriminant analysis, cluster analysis, simulation and the bootstrap, bump hunting, projection pursuit, and the estimation of hazard rates and other quantities that depend on the density. This book includes general survey of methods available for density estimation. The Kernel method, both for univariate and multivariate data, is discussed in detail, with particular emphasis on ways of deciding how much to smooth and on computation aspects. Attention is also given to adaptive methods, which smooth to a greater degree in the tails of the distribution, and to methods based on the idea of penalized likelihood.

Density Estimation for Statistics and Data Analysis

Bernard. W. Silverman
Density Estimation for Statistics and Data Analysis Book PDF

Author : Bernard. W. Silverman
Publisher : Routledge
Page : 105 pages
File Size : 48,6 Mb
Release : 2018-02-19
Category : Mathematics
ISBN : 9781351456166

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Density Estimation for Statistics and Data Analysis by Bernard. W. Silverman Pdf

Although there has been a surge of interest in density estimation in recent years, much of the published research has been concerned with purely technical matters with insufficient emphasis given to the technique's practical value. Furthermore, the subject has been rather inaccessible to the general statistician. The account presented in this book places emphasis on topics of methodological importance, in the hope that this will facilitate broader practical application of density estimation and also encourage research into relevant theoretical work. The book also provides an introduction to the subject for those with general interests in statistics. The important role of density estimation as a graphical technique is reflected by the inclusion of more than 50 graphs and figures throughout the text. Several contexts in which density estimation can be used are discussed, including the exploration and presentation of data, nonparametric discriminant analysis, cluster analysis, simulation and the bootstrap, bump hunting, projection pursuit, and the estimation of hazard rates and other quantities that depend on the density. This book includes general survey of methods available for density estimation. The Kernel method, both for univariate and multivariate data, is discussed in detail, with particular emphasis on ways of deciding how much to smooth and on computation aspects. Attention is also given to adaptive methods, which smooth to a greater degree in the tails of the distribution, and to methods based on the idea of penalized likelihood.

Multivariate Density Estimation

David W. Scott
Multivariate Density Estimation Book PDF

Author : David W. Scott
Publisher : John Wiley & Sons
Page : 384 pages
File Size : 48,8 Mb
Release : 2015-03-12
Category : Mathematics
ISBN : 9781118575482

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Multivariate Density Estimation by David W. Scott Pdf

Clarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods Featuring a thoroughly revised presentation, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the Second Edition presents additional clarification of theoretical opportunities, new algorithms, and up-to-date coverage of the unique challenges presented in the field of data analysis. The new edition focuses on the various density estimation techniques and methods that can be used in the field of big data. Defining optimal nonparametric estimators, the Second Edition demonstrates the density estimation tools to use when dealing with various multivariate structures in univariate, bivariate, trivariate, and quadrivariate data analysis. Continuing to illustrate the major concepts in the context of the classical histogram, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition also features: Over 150 updated figures to clarify theoretical results and to show analyses of real data sets An updated presentation of graphic visualization using computer software such as R A clear discussion of selections of important research during the past decade, including mixture estimation, robust parametric modeling algorithms, and clustering More than 130 problems to help readers reinforce the main concepts and ideas presented Boxed theorems and results allowing easy identification of crucial ideas Figures in color in the digital versions of the book A website with related data sets Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition is an ideal reference for theoretical and applied statisticians, practicing engineers, as well as readers interested in the theoretical aspects of nonparametric estimation and the application of these methods to multivariate data. The Second Edition is also useful as a textbook for introductory courses in kernel statistics, smoothing, advanced computational statistics, and general forms of statistical distributions.

Applied Smoothing Techniques for Data Analysis

Adrian W. Bowman,Adelchi Azzalini
Applied Smoothing Techniques for Data Analysis Book PDF

Author : Adrian W. Bowman,Adelchi Azzalini
Publisher : OUP Oxford
Page : 205 pages
File Size : 40,5 Mb
Release : 1997-08-14
Category : Mathematics
ISBN : 9780191545696

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Applied Smoothing Techniques for Data Analysis by Adrian W. Bowman,Adelchi Azzalini Pdf

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.

Graphics for Statistics and Data Analysis with R

Kevin J. Keen
Graphics for Statistics and Data Analysis with R Book PDF

Author : Kevin J. Keen
Publisher : CRC Press
Page : 508 pages
File Size : 43,8 Mb
Release : 2018-09-26
Category : Mathematics
ISBN : 9780429632211

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Graphics for Statistics and Data Analysis with R by Kevin J. Keen Pdf

Praise for the First Edition "The main strength of this book is that it provides a unified framework of graphical tools for data analysis, especially for univariate and low-dimensional multivariate data. In addition, it is clearly written in plain language and the inclusion of R code is particularly useful to assist readers’ understanding of the graphical techniques discussed in the book. ... It not only summarises graphical techniques, but it also serves as a practical reference for researchers and graduate students with an interest in data display." -Han Lin Shang, Journal of Applied Statistics Graphics for Statistics and Data Analysis with R, Second Edition, presents the basic principles of graphical design and applies these principles to engaging examples using the graphics and lattice packages in R. It offers a wide array of modern graphical displays for data visualization and representation. Added in the second edition are coverage of the ggplot2 graphics package, material on human visualization and color rendering in R, on screen, and in print. Features Emphasizes the fundamentals of statistical graphics and best practice guidelines for producing and choosing among graphical displays in R Presents technical details on topics such as: the estimation of quantiles, nonparametric and parametric density estimation; diagnostic plots for the simple linear regression model; polynomial regression, splines, and locally weighted polynomial regression for producing a smooth curve; Trellis graphics for multivariate data Provides downloadable R code and data for figures at www.graphicsforstatistics.com Kevin J. Keen is a Professor of Mathematics and Statistics at the University of Northern British Columbia (Prince George, Canada) and an Accredited Professional StatisticianTM by the Statistical Society of Canada and the American Statistical Association.

Smoothing of Multivariate Data

Jussi Sakari Klemelä
Smoothing of Multivariate Data Book PDF

Author : Jussi Sakari Klemelä
Publisher : John Wiley & Sons
Page : 641 pages
File Size : 53,5 Mb
Release : 2009-09-04
Category : Mathematics
ISBN : 9780470425664

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Smoothing of Multivariate Data by Jussi Sakari Klemelä Pdf

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.

Model-Based Clustering, Classification, and Density Estimation Using mclust in R

Luca Scrucca,Chris Fraley,T. Brendan Murphy,Raftery Adrian E.
Model Based Clustering Classification and Density Estimation Using mclust in R Book PDF

Author : Luca Scrucca,Chris Fraley,T. Brendan Murphy,Raftery Adrian E.
Publisher : CRC Press
Page : 269 pages
File Size : 42,7 Mb
Release : 2023-04-20
Category : Mathematics
ISBN : 9781000868340

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Model-Based Clustering, Classification, and Density Estimation Using mclust in R by Luca Scrucca,Chris Fraley,T. Brendan Murphy,Raftery Adrian E. Pdf

Model-Based Clustering, Classification, and Denisty Estimation Using mclust in R Model-based clustering and classification methods provide a systematic statistical approach to clustering, classification, and density estimation via mixture modeling. The model-based framework allows the problems of choosing or developing an appropriate clustering or classification method to be understood within the context of statistical modeling. The mclust package for the statistical environment R is a widely adopted platform implementing these model-based strategies. The package includes both summary and visual functionality, complementing procedures for estimating and choosing models. Key features of the book: An introduction to the model-based approach and the mclust R package A detailed description of mclust and the underlying modeling strategies An extensive set of examples, color plots, and figures along with the R code for reproducing them Supported by a companion website, including the R code to reproduce the examples and figures presented in the book, errata, and other supplementary material Model-Based Clustering, Classification, and Density Estimation Using mclust in R is accessible to quantitatively trained students and researchers with a basic understanding of statistical methods, including inference and computing. In addition to serving as a reference manual for mclust, the book will be particularly useful to those wishing to employ these model-based techniques in research or applications in statistics, data science, clinical research, social science, and many other disciplines.

Statistical Analysis Techniques in Particle Physics

Ilya Narsky,Frank C. Porter
Statistical Analysis Techniques in Particle Physics Book PDF

Author : Ilya Narsky,Frank C. Porter
Publisher : John Wiley & Sons
Page : 404 pages
File Size : 44,8 Mb
Release : 2013-10-24
Category : Science
ISBN : 9783527677290

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Statistical Analysis Techniques in Particle Physics by Ilya Narsky,Frank C. Porter Pdf

Modern analysis of HEP data needs advanced statistical tools to separate signal from background. This is the first book which focuses on machine learning techniques. It will be of interest to almost every high energy physicist, and, due to its coverage, suitable for students.

Handbook of Computational Statistics

James E. Gentle,Wolfgang Karl Härdle,Yuichi Mori
Handbook of Computational Statistics Book PDF

Author : James E. Gentle,Wolfgang Karl Härdle,Yuichi Mori
Publisher : Springer Science & Business Media
Page : 1180 pages
File Size : 51,9 Mb
Release : 2012-07-06
Category : Computers
ISBN : 9783642215513

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Handbook of Computational Statistics by James E. Gentle,Wolfgang Karl Härdle,Yuichi Mori Pdf

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.

Smoothing Methods in Statistics

Jeffrey S. Simonoff
Smoothing Methods in Statistics Book PDF

Author : Jeffrey S. Simonoff
Publisher : Springer Science & Business Media
Page : 356 pages
File Size : 41,7 Mb
Release : 1996-06-06
Category : Mathematics
ISBN : 9780387947167

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Smoothing Methods in Statistics by Jeffrey S. Simonoff Pdf

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 Methods in Statistics

Jeffrey S. Simonoff
Smoothing Methods in Statistics Book PDF

Author : Jeffrey S. Simonoff
Publisher : Springer Science & Business Media
Page : 349 pages
File Size : 50,8 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461240266

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Smoothing Methods in Statistics by Jeffrey S. Simonoff Pdf

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.

Transactions on Computational Science VI

Marina L. Gavrilova,C. J. Kenneth Tan
Transactions on Computational Science VI Book PDF

Author : Marina L. Gavrilova,C. J. Kenneth Tan
Publisher : Springer Science & Business Media
Page : 390 pages
File Size : 46,5 Mb
Release : 2009-12-09
Category : Computers
ISBN : 9783642106484

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Transactions on Computational Science VI by Marina L. Gavrilova,C. J. Kenneth Tan Pdf

This sixth volume of the Transactions on Computational Science journal contains the thoroughly refereed best papers selected from the International Conference on Computational Science and Its Applications, ICCSA 2008.

Nonparametric Density Estimation

Luc Devroye,László Györfi
Nonparametric Density Estimation Book PDF

Author : Luc Devroye,László Györfi
Publisher : New York ; Toronto : Wiley
Page : 376 pages
File Size : 42,6 Mb
Release : 1985-01-18
Category : Mathematics
ISBN : UOM:39015015715876

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Nonparametric Density Estimation by Luc Devroye,László Györfi Pdf

This book gives a rigorous, systematic treatment of density estimates, their construction, use and analysis with full proofs. It develops L1 theory, rather than the classical L2, showing how L1 exposes fundamental properties of density estimates masked by L2.

Road Traffic Modeling and Management

Fouzi Harrou,Abdelhafid Zeroual,Mohamad Mazen Hittawe,Ying Sun
Road Traffic Modeling and Management Book PDF

Author : Fouzi Harrou,Abdelhafid Zeroual,Mohamad Mazen Hittawe,Ying Sun
Publisher : Elsevier
Page : 270 pages
File Size : 46,6 Mb
Release : 2021-10-05
Category : Transportation
ISBN : 9780128234334

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Road Traffic Modeling and Management by Fouzi Harrou,Abdelhafid Zeroual,Mohamad Mazen Hittawe,Ying Sun Pdf

Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing road traffic monitoring and management. The book examines commonly used traffic analysis methodologies as well the emerging methods that use deep learning methods. Other sections discuss how to understand statistical models and machine learning algorithms and how to apply them to traffic modeling, estimation, forecasting and traffic congestion monitoring. Providing both a theoretical framework along with practical technical solutions, this book is ideal for researchers and practitioners who want to improve the performance of intelligent transportation systems. Provides integrated, up-to-date and complete coverage of the key components for intelligent transportation systems: traffic modeling, forecasting, estimation and monitoring Uses methods based on video and time series data for traffic modeling and forecasting Includes case studies, key processes guidance and comparisons of different methodologies

Semiparametric and Nonparametric Econometrics

Aman Ullah
Semiparametric and Nonparametric Econometrics Book PDF

Author : Aman Ullah
Publisher : Springer Science & Business Media
Page : 180 pages
File Size : 45,7 Mb
Release : 2012-12-06
Category : Business & Economics
ISBN : 9783642518485

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Semiparametric and Nonparametric Econometrics by Aman Ullah Pdf

Over the last three decades much research in empirical and theoretical economics has been carried on under various assumptions. For example a parametric functional form of the regression model, the heteroskedasticity, and the autocorrelation is always as sumed, usually linear. Also, the errors are assumed to follow certain parametric distri butions, often normal. A disadvantage of parametric econometrics based on these assumptions is that it may not be robust to the slight data inconsistency with the particular parametric specification. Indeed any misspecification in the functional form may lead to erroneous conclusions. In view of these problems, recently there has been significant interest in 'the semiparametric/nonparametric approaches to econometrics. The semiparametric approach considers econometric models where one component has a parametric and the other, which is unknown, a nonparametric specification (Manski 1984 and Horowitz and Neumann 1987, among others). The purely non parametric approach, on the other hand, does not specify any component of the model a priori. The main ingredient of this approach is the data based estimation of the unknown joint density due to Rosenblatt (1956). Since then, especially in the last decade, a vast amount of literature has appeared on nonparametric estimation in statistics journals. However, this literature is mostly highly technical and this may partly be the reason why very little is known about it in econometrics, although see Bierens (1987) and Ullah (1988).