Smoothing In Discrete Multivariate Analysis
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Author |
: Prabir Burman |
Publisher |
: |
Total Pages |
: 66 |
Release |
: 1985 |
ISBN-10 |
: OCLC:84451179 |
ISBN-13 |
: |
Rating |
: 4/5 (79 Downloads) |
Synopsis Smoothing in discrete multivariate analysis by : Prabir Burman
Author |
: Yvonne M. Bishop |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 558 |
Release |
: 2007-07-30 |
ISBN-10 |
: 9780387728056 |
ISBN-13 |
: 0387728058 |
Rating |
: 4/5 (56 Downloads) |
Synopsis Discrete Multivariate Analysis by : Yvonne M. Bishop
“A welcome addition to multivariate analysis. The discussion is lucid and very leisurely, excellently illustrated with applications drawn from a wide variety of fields. A good part of the book can be understood without very specialized statistical knowledge. It is a most welcome contribution to an interesting and lively subject.” -- Nature Originally published in 1974, this book is a reprint of a classic, still-valuable text.
Author |
: Jussi Sakari Klemelä |
Publisher |
: John Wiley & Sons |
Total Pages |
: 641 |
Release |
: 2009-09-04 |
ISBN-10 |
: 9780470425664 |
ISBN-13 |
: 0470425660 |
Rating |
: 4/5 (64 Downloads) |
Synopsis Smoothing of Multivariate Data by : Jussi Sakari Klemelä
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.
Author |
: Jeffrey S. Simonoff |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 349 |
Release |
: 2012-12-06 |
ISBN-10 |
: 9781461240266 |
ISBN-13 |
: 1461240263 |
Rating |
: 4/5 (66 Downloads) |
Synopsis Smoothing Methods in Statistics by : Jeffrey S. Simonoff
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.
Author |
: Yvonne M. M. Bishop |
Publisher |
: |
Total Pages |
: 557 |
Release |
: 1980 |
ISBN-10 |
: OCLC:243903865 |
ISBN-13 |
: |
Rating |
: 4/5 (65 Downloads) |
Synopsis Discrete multivariate analysis by : Yvonne M. M. Bishop
Author |
: Thomas J. Santner |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 381 |
Release |
: 2012-12-06 |
ISBN-10 |
: 9781461210177 |
ISBN-13 |
: 1461210178 |
Rating |
: 4/5 (77 Downloads) |
Synopsis The Statistical Analysis of Discrete Data by : Thomas J. Santner
The Statistical Analysis of Discrete Data provides an introduction to cur rent statistical methods for analyzing discrete response data. The book can be used as a course text for graduate students and as a reference for researchers who analyze discrete data. The book's mathematical prereq uisites are linear algebra and elementary advanced calculus. It assumes a basic statistics course which includes some decision theory, and knowledge of classical linear model theory for continuous response data. Problems are provided at the end of each chapter to give the reader an opportunity to ap ply the methods in the text, to explore extensions of the material covered, and to analyze data with discrete responses. In the text examples, and in the problems, we have sought to include interesting data sets from a wide variety of fields including political science, medicine, nuclear engineering, sociology, ecology, cancer research, library science, and biology. Although there are several texts available on discrete data analysis, we felt there was a need for a book which incorporated some of the myriad recent research advances. Our motivation was to introduce the subject by emphasizing its ties to the well-known theories of linear models, experi mental design, and regression diagnostics, as well as to describe alterna tive methodologies (Bayesian, smoothing, etc. ); the latter are based on the premise that external information is available. These overriding goals, to gether with our own experiences and biases, have governed our choice of topics.
Author |
: Timothy R.C. Read |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 221 |
Release |
: 2012-12-06 |
ISBN-10 |
: 9781461245780 |
ISBN-13 |
: 1461245788 |
Rating |
: 4/5 (80 Downloads) |
Synopsis Goodness-of-Fit Statistics for Discrete Multivariate Data by : Timothy R.C. Read
The statistical analysis of discrete multivariate data has received a great deal of attention in the statistics literature over the past two decades. The develop ment ofappropriate models is the common theme of books such as Cox (1970), Haberman (1974, 1978, 1979), Bishop et al. (1975), Gokhale and Kullback (1978), Upton (1978), Fienberg (1980), Plackett (1981), Agresti (1984), Goodman (1984), and Freeman (1987). The objective of our book differs from those listed above. Rather than concentrating on model building, our intention is to describe and assess the goodness-of-fit statistics used in the model verification part of the inference process. Those books that emphasize model development tend to assume that the model can be tested with one of the traditional goodness-of-fit tests 2 2 (e.g., Pearson's X or the loglikelihood ratio G ) using a chi-squared critical value. However, it is well known that this can give a poor approximation in many circumstances. This book provides the reader with a unified analysis of the traditional goodness-of-fit tests, describing their behavior and relative merits as well as introducing some new test statistics. The power-divergence family of statistics (Cressie and Read, 1984) is used to link the traditional test statistics through a single real-valued parameter, and provides a way to consolidate and extend the current fragmented literature. As a by-product of our analysis, a new 2 2 statistic emerges "between" Pearson's X and the loglikelihood ratio G that has some valuable properties.
Author |
: Yvonne Millicent Mahala Bishop |
Publisher |
: MIT Press (MA) |
Total Pages |
: 576 |
Release |
: 1975 |
ISBN-10 |
: UCLA:L0086626736 |
ISBN-13 |
: |
Rating |
: 4/5 (36 Downloads) |
Synopsis Discrete Multivariate Analysis: Theory and Practice by : Yvonne Millicent Mahala Bishop
Structural models for counted data; Maximum likelihood estimates for complete tables; Formal goodness of fit: summary statistics and model selection; Maximum likelihood estimation for incomplete tables; Estimating the size of a closed population; Models for measuring change; Analysis of square tables: symmetry and marginal homogeneity; Model selection and assessing closeness of fit: practical aspects; Other methods for estimation and testing in cross-classifications; Measures of association and agreement; Pseudo-bayes estimates of cell probabilities; Sampling models for discrete data; Asymptotic methods.
Author |
: José E. Chacón |
Publisher |
: CRC Press |
Total Pages |
: 226 |
Release |
: 2018-05-08 |
ISBN-10 |
: 9780429939143 |
ISBN-13 |
: 0429939140 |
Rating |
: 4/5 (43 Downloads) |
Synopsis Multivariate Kernel Smoothing and Its Applications by : José E. Chacón
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.
Author |
: David W. Scott |
Publisher |
: John Wiley & Sons |
Total Pages |
: 384 |
Release |
: 2015-03-12 |
ISBN-10 |
: 9781118575536 |
ISBN-13 |
: 1118575539 |
Rating |
: 4/5 (36 Downloads) |
Synopsis Multivariate Density Estimation by : David W. Scott
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.