Multivariate Kernel Smoothing And Its Applications

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Multivariate Kernel Smoothing and Its Applications

José E. Chacón,Tarn Duong
Multivariate Kernel Smoothing and Its Applications Book PDF

Author : José E. Chacón,Tarn Duong
Publisher : CRC Press
Page : 226 pages
File Size : 48,8 Mb
Release : 2018-05-08
Category : Mathematics
ISBN : 9780429939143

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Multivariate Kernel Smoothing and Its Applications by José E. Chacón,Tarn Duong Pdf

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

Sucharita Ghosh
Kernel Smoothing Book PDF

Author : Sucharita Ghosh
Publisher : John Wiley & Sons
Page : 272 pages
File Size : 47,7 Mb
Release : 2018-01-09
Category : Mathematics
ISBN : 9781118456057

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Kernel Smoothing by Sucharita Ghosh Pdf

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.

Kernel Smoothing

M.P. Wand,M.C. Jones
Kernel Smoothing Book PDF

Author : M.P. Wand,M.C. Jones
Publisher : CRC Press
Page : 230 pages
File Size : 47,5 Mb
Release : 1994-12-01
Category : Mathematics
ISBN : 0412552701

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Kernel Smoothing by M.P. Wand,M.C. Jones Pdf

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 facilitated by the authors' focus on the simplest settings, namely density estimation and nonparametric regression. They pay particular attention to the problem of choosing the smoothing parameter of a kernel smoother, and also treat the multivariate case in detail. Kernal Smoothing is self-contained and assumes only a basic knowledge of statistics, calculus, and matrix algebra. It is an invaluable introduction to the main ideas of kernel estimation for students and researchers from other discipline and provides a comprehensive reference for those familiar with the topic.

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 : 42,7 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.

Kernel Smoothing

M. P. Wand,M. C. Jones
Kernel Smoothing Book PDF

Author : M. P. Wand,M. C. Jones
Publisher : Springer
Page : 212 pages
File Size : 50,5 Mb
Release : 1995-01-01
Category : Computers
ISBN : 0412552701

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Kernel Smoothing by M. P. Wand,M. C. Jones Pdf

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 facilitated by the authors' focus on the simplest settings, namely density estimation and nonparametric regression. They pay particular attention to the problem of choosing the smoothing parameter of a kernel smoother, and also treat the multivariate case in detail. Kernal Smoothing is self-contained and assumes only a basic knowledge of statistics, calculus, and matrix algebra. It is an invaluable introduction to the main ideas of kernel estimation for students and researchers from other discipline and provides a comprehensive reference for those familiar with the topic.

Nonparametric Econometrics

Qi Li,Jeffrey Scott Racine
Nonparametric Econometrics Book PDF

Author : Qi Li,Jeffrey Scott Racine
Publisher : Princeton University Press
Page : 768 pages
File Size : 55,6 Mb
Release : 2023-07-18
Category : Business & Economics
ISBN : 9780691248080

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Nonparametric Econometrics by Qi Li,Jeffrey Scott Racine Pdf

A comprehensive, up-to-date textbook on nonparametric methods for students and researchers Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. Nonparametric Econometrics fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers. Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data—nominal and ordinal—in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory. This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types—continuous, nominal, and ordinal—within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables. Nonparametric Econometrics covers all the material necessary to understand and apply nonparametric methods for real-world problems.

Kernel Smoothing in MATLAB

Ivana Horová,Jan Koláček,Jiří Zelinka
Kernel Smoothing in MATLAB Book PDF

Author : Ivana Horová,Jan Koláček,Jiří Zelinka
Publisher : World Scientific
Page : 242 pages
File Size : 52,8 Mb
Release : 2012
Category : Mathematics
ISBN : 9789814405485

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Kernel Smoothing in MATLAB by Ivana Horová,Jan Koláček,Jiří Zelinka Pdf

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.

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 : 43,7 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.

Multivariate Density Estimation

David W. Scott
Multivariate Density Estimation Book PDF

Author : David W. Scott
Publisher : John Wiley & Sons
Page : 358 pages
File Size : 48,7 Mb
Release : 1992-08-31
Category : Mathematics
ISBN : 0471547700

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Multivariate Density Estimation by David W. Scott Pdf

Representation and geometry of multivariate data; Nonparametric estimation criteria; Histograms: theory and practice; Frequency polygons; Averaged shifted histograms; Kernel density estimators; The curse of dimensionality and dimension reduction; Nonparametric regression and additive models; Other applications.

The Statistical Analysis of Multivariate Failure Time Data

Ross L. Prentice,Shanshan Zhao
The Statistical Analysis of Multivariate Failure Time Data Book PDF

Author : Ross L. Prentice,Shanshan Zhao
Publisher : CRC Press
Page : 110 pages
File Size : 48,7 Mb
Release : 2019-05-14
Category : Mathematics
ISBN : 9780429529702

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The Statistical Analysis of Multivariate Failure Time Data by Ross L. Prentice,Shanshan Zhao Pdf

The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of regression information. For example, in a context of randomized trial or cohort studies, the results go beyond that obtained by analyzing each failure time outcome in a univariate fashion. The book is addressed to researchers, practitioners, and graduate students, and can be used as a reference or as a graduate course text. Much of the literature on the analysis of censored correlated failure time data uses frailty or copula models to allow for residual dependencies among failure times, given covariates. In contrast, this book provides a detailed account of recently developed methods for the simultaneous estimation of marginal single and dual outcome hazard rate regression parameters, with emphasis on multiplicative (Cox) models. Illustrations are provided of the utility of these methods using Women’s Health Initiative randomized controlled trial data of menopausal hormones and of a low-fat dietary pattern intervention. As byproducts, these methods provide flexible semiparametric estimators of pairwise bivariate survivor functions at specified covariate histories, as well as semiparametric estimators of cross ratio and concordance functions given covariates. The presentation also describes how these innovative methods may extend to handle issues of dependent censorship, missing and mismeasured covariates, and joint modeling of failure times and covariates, setting the stage for additional theoretical and applied developments. This book extends and continues the style of the classic Statistical Analysis of Failure Time Data by Kalbfleisch and Prentice. Ross L. Prentice is Professor of Biostatistics at the Fred Hutchinson Cancer Research Center and University of Washington in Seattle, Washington. He is the recipient of COPSS Presidents and Fisher awards, the AACR Epidemiology/Prevention and Team Science awards, and is a member of the National Academy of Medicine. Shanshan Zhao is a Principal Investigator at the National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina.

Data Science: Theory and Applications

Anonim
Data Science Theory and Applications Book PDF

Author : Anonim
Publisher : Elsevier
Page : 350 pages
File Size : 49,9 Mb
Release : 2021-02-12
Category : Mathematics
ISBN : 9780323852012

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Data Science: Theory and Applications by Anonim Pdf

Data Science: Theory and Applications, Volume 44 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of interesting topics, including Modeling extreme climatic events using the generalized extreme value distribution, Bayesian Methods in Data Science, Mathematical Modeling in Health Economic Evaluations, Data Science in Cancer Genomics, Blockchain Technology: Theory and Practice, Statistical outline of animal home ranges, an application of set estimation, Application of Data Handling Techniques to Predict Pavement Performance, Analysis of individual treatment effects for enhanced inferences in medicine, and more. Additional sections cover Nonparametric Data Science: Testing Hypotheses in Large Complex Data, From Urban Mobility Problems to Data Science Solutions, and Data Structures and Artificial Intelligence Methods. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Handbook of Statistics series Updated release includes the latest information on Data Science: Theory and Applications

Privacy in Statistical Databases

Josep Domingo-Ferrer,Krishnamurty Muralidhar
Privacy in Statistical Databases Book PDF

Author : Josep Domingo-Ferrer,Krishnamurty Muralidhar
Publisher : Springer Nature
Page : 371 pages
File Size : 47,7 Mb
Release : 2020-09-16
Category : Computers
ISBN : 9783030575212

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Privacy in Statistical Databases by Josep Domingo-Ferrer,Krishnamurty Muralidhar Pdf

This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2020, held in Tarragona, Spain, in September 2020 under the sponsorship of the UNESCO Chair in Data Privacy. The 25 revised full papers presented were carefully reviewed and selected from 49 submissions. The papers are organized into the following topics: privacy models; microdata protection; protection of statistical tables; protection of interactive and mobility databases; record linkage and alternative methods; synthetic data; data quality; and case studies. The Chapter “Explaining recurrent machine learning models: integral privacy revisited” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Evolutionary Multi-Criterion Optimization

Hisao Ishibuchi,Qingfu Zhang,Ran Cheng,Ke Li,Hui Li,Handing Wang,Aimin Zhou
Evolutionary Multi Criterion Optimization Book PDF

Author : Hisao Ishibuchi,Qingfu Zhang,Ran Cheng,Ke Li,Hui Li,Handing Wang,Aimin Zhou
Publisher : Springer Nature
Page : 781 pages
File Size : 51,5 Mb
Release : 2021-03-24
Category : Computers
ISBN : 9783030720629

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Evolutionary Multi-Criterion Optimization by Hisao Ishibuchi,Qingfu Zhang,Ran Cheng,Ke Li,Hui Li,Handing Wang,Aimin Zhou Pdf

This book constitutes the refereed proceedings of the 11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021 held in Shenzhen, China, in March 2021. The 47 full papers and 14 short papers were carefully reviewed and selected from 120 submissions. The papers are divided into the following topical sections: theory; algorithms; dynamic multi-objective optimization; constrained multi-objective optimization; multi-modal optimization; many-objective optimization; performance evaluations and empirical studies; EMO and machine learning; surrogate modeling and expensive optimization; MCDM and interactive EMO; and applications.

Introduction to Time Series Modeling with Applications in R

Genshiro Kitagawa
Introduction to Time Series Modeling with Applications in R Book PDF

Author : Genshiro Kitagawa
Publisher : CRC Press
Page : 262 pages
File Size : 48,9 Mb
Release : 2020-08-10
Category : Mathematics
ISBN : 9780429582622

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Introduction to Time Series Modeling with Applications in R by Genshiro Kitagawa Pdf

Praise for the first edition: [This book] reflects the extensive experience and significant contributions of the author to non-linear and non-Gaussian modeling. ... [It] is a valuable book, especially with its broad and accessible introduction of models in the state-space framework. –Statistics in Medicine What distinguishes this book from comparable introductory texts is the use of state-space modeling. Along with this come a number of valuable tools for recursive filtering and smoothing, including the Kalman filter, as well as non-Gaussian and sequential Monte Carlo filters. –MAA Reviews Introduction to Time Series Modeling with Applications in R, Second Edition covers numerous stationary and nonstationary time series models and tools for estimating and utilizing them. The goal of this book is to enable readers to build their own models to understand, predict and master time series. The second edition makes it possible for readers to reproduce examples in this book by using the freely available R package TSSS to perform computations for their own real-world time series problems. This book employs the state-space model as a generic tool for time series modeling and presents the Kalman filter, the non-Gaussian filter and the particle filter as convenient tools for recursive estimation for state-space models. Further, it also takes a unified approach based on the entropy maximization principle and employs various methods of parameter estimation and model selection, including the least squares method, the maximum likelihood method, recursive estimation for state-space models and model selection by AIC. Along with the standard stationary time series models, such as the AR and ARMA models, the book also introduces nonstationary time series models such as the locally stationary AR model, the trend model, the seasonal adjustment model, the time-varying coefficient AR model and nonlinear non-Gaussian state-space models. About the Author: Genshiro Kitagawa is a project professor at the University of Tokyo, the former Director-General of the Institute of Statistical Mathematics, and the former President of the Research Organization of Information and Systems.

Multivariate Nonparametric Regression and Visualization

Jussi Sakari Klemelä
Multivariate Nonparametric Regression and Visualization Book PDF

Author : Jussi Sakari Klemelä
Publisher : John Wiley & Sons
Page : 392 pages
File Size : 40,8 Mb
Release : 2014-05-05
Category : Mathematics
ISBN : 9781118593509

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Multivariate Nonparametric Regression and Visualization by Jussi Sakari Klemelä Pdf

A modern approach to statistical learning and its applications through visualization methods With a unique and innovative presentation, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions when given a set of data. Focusing on nonparametric methods to adapt to the multiple types of data generating mechanisms, the book begins with an overview of classification and regression. The book then introduces and examines various tested and proven visualization techniques for learning samples and functions. Multivariate Nonparametric Regression and Visualization identifies risk management, portfolio selection, and option pricing as the main areas in which statistical methods may be implemented in quantitative finance. The book provides coverage of key statistical areas including linear methods, kernel methods, additive models and trees, boosting, support vector machines, and nearest neighbor methods. Exploring the additional applications of nonparametric and semiparametric methods, Multivariate Nonparametric Regression and Visualization features: An extensive appendix with R-package training material to encourage duplication and modification of the presented computations and research Multiple examples to demonstrate the applications in the field of finance Sections with formal definitions of the various applied methods for readers to utilize throughout the book Multivariate Nonparametric Regression and Visualization is an ideal textbook for upper-undergraduate and graduate-level courses on nonparametric function estimation, advanced topics in statistics, and quantitative finance. The book is also an excellent reference for practitioners who apply statistical methods in quantitative finance.