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Nonparametric Functional Estimation

Author: B. L. S. Prakasa Rao

Publisher: Academic Press

Published: 2014-07-10

Total Pages: 538

ISBN-13: 148326923X

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Nonparametric Functional Estimation is a compendium of papers, written by experts, in the area of nonparametric functional estimation. This book attempts to be exhaustive in nature and is written both for specialists in the area as well as for students of statistics taking courses at the postgraduate level. The main emphasis throughout the book is on the discussion of several methods of estimation and on the study of their large sample properties. Chapters are devoted to topics on estimation of density and related functions, the application of density estimation to classification problems, and the different facets of estimation of distribution functions. Statisticians and students of statistics and engineering will find the text very useful.

Book Synopsis Nonparametric Functional Estimation by : B. L. S. Prakasa Rao

Download or read book Nonparametric Functional Estimation written by B. L. S. Prakasa Rao and published by Academic Press. This book was released on 2014-07-10 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonparametric Functional Estimation is a compendium of papers, written by experts, in the area of nonparametric functional estimation. This book attempts to be exhaustive in nature and is written both for specialists in the area as well as for students of statistics taking courses at the postgraduate level. The main emphasis throughout the book is on the discussion of several methods of estimation and on the study of their large sample properties. Chapters are devoted to topics on estimation of density and related functions, the application of density estimation to classification problems, and the different facets of estimation of distribution functions. Statisticians and students of statistics and engineering will find the text very useful.

Nonparametric Functional Estimation and Related Topics

Author: G.G Roussas

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 691

ISBN-13: 9401132224

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

Book Synopsis Nonparametric Functional Estimation and Related Topics by : G.G Roussas

Download or read book Nonparametric Functional Estimation and Related Topics written by G.G Roussas and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 691 pages. Available in PDF, EPUB and Kindle. Book excerpt: About three years ago, an idea was discussed among some colleagues in the Division of Statistics at the University of California, Davis, as to the possibility of holding an international conference, focusing exclusively on nonparametric curve estimation. The fruition of this idea came about with the enthusiastic support of this project by Luc Devroye of McGill University, Canada, and Peter Robinson of the London School of Economics, UK. The response of colleagues, contacted to ascertain interest in participation in such a conference, was gratifying and made the effort involved worthwhile. Devroye and Robinson, together with this editor and George Metakides of the University of Patras, Greece and of the European Economic Communities, Brussels, formed the International Organizing Committee for a two week long Advanced Study Institute (ASI) sponsored by the Scientific Affairs Division of the North Atlantic Treaty Organization (NATO). The ASI was held on the Greek Island of Spetses between July 29 and August 10, 1990. Nonparametric functional estimation is a central topic in statistics, with applications in numerous substantive fields in mathematics, natural and social sciences, engineering and medicine. While there has been interest in nonparametric functional estimation for many years, this has grown of late, owing to increasing availability of large data sets and the ability to process them by means of improved computing facilities, along with the ability to display the results by means of sophisticated graphical procedures.

Introduction to Nonparametric Estimation

Author: Alexandre B. Tsybakov

Publisher: Springer Science & Business Media

Published: 2008-10-22

Total Pages: 222

ISBN-13: 0387790527

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Developed from lecture notes and ready to be used for a course on the graduate level, this concise text aims to introduce the fundamental concepts of nonparametric estimation theory while maintaining the exposition suitable for a first approach in the field.

Book Synopsis Introduction to Nonparametric Estimation by : Alexandre B. Tsybakov

Download or read book Introduction to Nonparametric Estimation written by Alexandre B. Tsybakov and published by Springer Science & Business Media. This book was released on 2008-10-22 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Developed from lecture notes and ready to be used for a course on the graduate level, this concise text aims to introduce the fundamental concepts of nonparametric estimation theory while maintaining the exposition suitable for a first approach in the field.

Nonparametric Function Estimation, Modeling, and Simulation

Author: James R. Thompson

Publisher: SIAM

Published: 1990-01-01

Total Pages: 320

ISBN-13: 9781611971712

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Topics emphasized include nonparametric density estimation as an exploratory device plus the deeper models to which the exploratory analysis points, multi-dimensional data analysis, and analysis of remote sensing data, cancer progression, chaos theory, epidemiological modeling, and parallel based algorithms. New methods discussed are quick nonparametric density estimation based techniques for resampling and simulation based estimation techniques not requiring closed form solutions.

Book Synopsis Nonparametric Function Estimation, Modeling, and Simulation by : James R. Thompson

Download or read book Nonparametric Function Estimation, Modeling, and Simulation written by James R. Thompson and published by SIAM. This book was released on 1990-01-01 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Topics emphasized include nonparametric density estimation as an exploratory device plus the deeper models to which the exploratory analysis points, multi-dimensional data analysis, and analysis of remote sensing data, cancer progression, chaos theory, epidemiological modeling, and parallel based algorithms. New methods discussed are quick nonparametric density estimation based techniques for resampling and simulation based estimation techniques not requiring closed form solutions.

Nonparametric Functional Data Analysis

Author: Frédéric Ferraty

Publisher: Springer Science & Business Media

Published: 2006-11-22

Total Pages: 260

ISBN-13: 0387366202

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Modern apparatuses allow us to collect samples of functional data, mainly curves but also images. On the other hand, nonparametric statistics produces useful tools for standard data exploration. This book links these two fields of modern statistics by explaining how functional data can be studied through parameter-free statistical ideas. At the same time it shows how functional data can be studied through parameter-free statistical ideas, and offers an original presentation of new nonparametric statistical methods for functional data analysis.

Book Synopsis Nonparametric Functional Data Analysis by : Frédéric Ferraty

Download or read book Nonparametric Functional Data Analysis written by Frédéric Ferraty and published by Springer Science & Business Media. This book was released on 2006-11-22 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern apparatuses allow us to collect samples of functional data, mainly curves but also images. On the other hand, nonparametric statistics produces useful tools for standard data exploration. This book links these two fields of modern statistics by explaining how functional data can be studied through parameter-free statistical ideas. At the same time it shows how functional data can be studied through parameter-free statistical ideas, and offers an original presentation of new nonparametric statistical methods for functional data analysis.

Nonparametric Curve Estimation

Author: Sam Efromovich

Publisher: Springer Science & Business Media

Published: 2008-01-19

Total Pages: 414

ISBN-13: 0387226389

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This book gives a systematic, comprehensive, and unified account of modern nonparametric statistics of density estimation, nonparametric regression, filtering signals, and time series analysis. The companion software package, available over the Internet, brings all of the discussed topics into the realm of interactive research. Virtually every claim and development mentioned in the book is illustrated with graphs which are available for the reader to reproduce and modify, making the material fully transparent and allowing for complete interactivity.

Book Synopsis Nonparametric Curve Estimation by : Sam Efromovich

Download or read book Nonparametric Curve Estimation written by Sam Efromovich and published by Springer Science & Business Media. This book was released on 2008-01-19 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives a systematic, comprehensive, and unified account of modern nonparametric statistics of density estimation, nonparametric regression, filtering signals, and time series analysis. The companion software package, available over the Internet, brings all of the discussed topics into the realm of interactive research. Virtually every claim and development mentioned in the book is illustrated with graphs which are available for the reader to reproduce and modify, making the material fully transparent and allowing for complete interactivity.

Nonparametric Kernel Density Estimation and Its Computational Aspects

Author: Artur Gramacki

Publisher: Springer

Published: 2017-12-21

Total Pages: 176

ISBN-13: 3319716883

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This book describes computational problems related to kernel density estimation (KDE) – one of the most important and widely used data smoothing techniques. A very detailed description of novel FFT-based algorithms for both KDE computations and bandwidth selection are presented. The theory of KDE appears to have matured and is now well developed and understood. However, there is not much progress observed in terms of performance improvements. This book is an attempt to remedy this. The book primarily addresses researchers and advanced graduate or postgraduate students who are interested in KDE and its computational aspects. The book contains both some background and much more sophisticated material, hence also more experienced researchers in the KDE area may find it interesting. The presented material is richly illustrated with many numerical examples using both artificial and real datasets. Also, a number of practical applications related to KDE are presented.

Book Synopsis Nonparametric Kernel Density Estimation and Its Computational Aspects by : Artur Gramacki

Download or read book Nonparametric Kernel Density Estimation and Its Computational Aspects written by Artur Gramacki and published by Springer. This book was released on 2017-12-21 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes computational problems related to kernel density estimation (KDE) – one of the most important and widely used data smoothing techniques. A very detailed description of novel FFT-based algorithms for both KDE computations and bandwidth selection are presented. The theory of KDE appears to have matured and is now well developed and understood. However, there is not much progress observed in terms of performance improvements. This book is an attempt to remedy this. The book primarily addresses researchers and advanced graduate or postgraduate students who are interested in KDE and its computational aspects. The book contains both some background and much more sophisticated material, hence also more experienced researchers in the KDE area may find it interesting. The presented material is richly illustrated with many numerical examples using both artificial and real datasets. Also, a number of practical applications related to KDE are presented.

Selected Works of Murray Rosenblatt

Author: Richard A. Davis

Publisher: Springer Science & Business Media

Published: 2011-05-06

Total Pages: 496

ISBN-13: 1441983392

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During the second half of the 20th century, Murray Rosenblatt was one of the most celebrated and leading figures in probability and statistics. Among his many contributions, Rosenblatt conducted seminal work on density estimation, central limit theorems under strong mixing conditions, spectral domain methodology, long memory processes and Markov processes. He has published over 130 papers and 5 books, many as relevant today as when they first appeared decades ago. Murray Rosenblatt was one of the founding members of the Department of Mathematics at the University of California at San Diego (UCSD) and served as advisor to over twenty PhD students. He maintains a close association with UCSD in his role as Professor Emeritus. This volume is a celebration of Murray Rosenblatt's stellar research career that spans over six decades, and includes some of his most interesting and influential papers. Several leading experts provide commentary and reflections on various directions of Murray's research portfolio.

Book Synopsis Selected Works of Murray Rosenblatt by : Richard A. Davis

Download or read book Selected Works of Murray Rosenblatt written by Richard A. Davis and published by Springer Science & Business Media. This book was released on 2011-05-06 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the second half of the 20th century, Murray Rosenblatt was one of the most celebrated and leading figures in probability and statistics. Among his many contributions, Rosenblatt conducted seminal work on density estimation, central limit theorems under strong mixing conditions, spectral domain methodology, long memory processes and Markov processes. He has published over 130 papers and 5 books, many as relevant today as when they first appeared decades ago. Murray Rosenblatt was one of the founding members of the Department of Mathematics at the University of California at San Diego (UCSD) and served as advisor to over twenty PhD students. He maintains a close association with UCSD in his role as Professor Emeritus. This volume is a celebration of Murray Rosenblatt's stellar research career that spans over six decades, and includes some of his most interesting and influential papers. Several leading experts provide commentary and reflections on various directions of Murray's research portfolio.

Functional Estimation For Density, Regression Models And Processes (Second Edition)

Author: Odile Pons

Publisher: World Scientific

Published: 2023-09-22

Total Pages: 259

ISBN-13: 9811272859

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Nonparametric kernel estimators apply to the statistical analysis of independent or dependent sequences of random variables and for samples of continuous or discrete processes. The optimization of these procedures is based on the choice of a bandwidth that minimizes an estimation error and the weak convergence of the estimators is proved. This book introduces new mathematical results on statistical methods for the density and regression functions presented in the mathematical literature and for functions defining more complex models such as the models for the intensity of point processes, for the drift and variance of auto-regressive diffusions and the single-index regression models.This second edition presents minimax properties with Lp risks, for a real p larger than one, and optimal convergence results for new kernel estimators of function defining processes: models for multidimensional variables, periodic intensities, estimators of the distribution functions of censored and truncated variables, estimation in frailty models, estimators for time dependent diffusions, for spatial diffusions and for diffusions with stochastic volatility.

Book Synopsis Functional Estimation For Density, Regression Models And Processes (Second Edition) by : Odile Pons

Download or read book Functional Estimation For Density, Regression Models And Processes (Second Edition) written by Odile Pons and published by World Scientific. This book was released on 2023-09-22 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonparametric kernel estimators apply to the statistical analysis of independent or dependent sequences of random variables and for samples of continuous or discrete processes. The optimization of these procedures is based on the choice of a bandwidth that minimizes an estimation error and the weak convergence of the estimators is proved. This book introduces new mathematical results on statistical methods for the density and regression functions presented in the mathematical literature and for functions defining more complex models such as the models for the intensity of point processes, for the drift and variance of auto-regressive diffusions and the single-index regression models.This second edition presents minimax properties with Lp risks, for a real p larger than one, and optimal convergence results for new kernel estimators of function defining processes: models for multidimensional variables, periodic intensities, estimators of the distribution functions of censored and truncated variables, estimation in frailty models, estimators for time dependent diffusions, for spatial diffusions and for diffusions with stochastic volatility.

Functional Estimation for Density, Regression Models and Processes

Author: Odile Pons

Publisher: World Scientific

Published: 2011-03-21

Total Pages: 212

ISBN-13: 9814460613

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This book presents a unified approach on nonparametric estimators for models of independent observations, jump processes and continuous processes. New estimators are defined and their limiting behavior is studied. From a practical point of view, the book expounds on the construction of estimators for functionals of processes and densities, and provides asymptotic expansions and optimality properties from smooth estimators. It also presents new regular estimators for functionals of processes, compares histogram and kernel estimators, compares several new estimators for single-index models, and it examines the weak convergence of the estimators. Contents:IntroductionKernel Estimator of a DensityKernel Estimator of a Regression FunctionLimits for the Varying Bandwidths EstimatorsNonparametric Estimation of QuantilesNonparametric Estimation of Intensities for Stochastic ProcessesEstimation in Semi-Parametric Regression ModelsDiffusion ProcessesApplications to Time Series Readership: Advanced undergraduate and graduate students in mathematical statistics and computational statistics; researchers in mathematical or applied statistics; statisticians. Keywords:Kernel Estimation;Density;Regression;Intensity;Diffusion;Nonparametric;Weak Convergence;Ergodic Process;Intensity of Point Process;Kernel Estimation;Single-Index Models;Functional Time Series;Variable BandwidthKey Features:Covers a wide range of nonparametric models and presents new functional estimatorsContains a detailed presentation of the mathematical techniques for functional estimation with recent advances in their optimizationA valuable resource for scientific researchers who model observation dataReviews: “This book is useful for researchers interested in the study of asymptotic properties of different types of optimum estimators obtained through the method of kernels.” Mathematical Reviews

Book Synopsis Functional Estimation for Density, Regression Models and Processes by : Odile Pons

Download or read book Functional Estimation for Density, Regression Models and Processes written by Odile Pons and published by World Scientific. This book was released on 2011-03-21 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a unified approach on nonparametric estimators for models of independent observations, jump processes and continuous processes. New estimators are defined and their limiting behavior is studied. From a practical point of view, the book expounds on the construction of estimators for functionals of processes and densities, and provides asymptotic expansions and optimality properties from smooth estimators. It also presents new regular estimators for functionals of processes, compares histogram and kernel estimators, compares several new estimators for single-index models, and it examines the weak convergence of the estimators. Contents:IntroductionKernel Estimator of a DensityKernel Estimator of a Regression FunctionLimits for the Varying Bandwidths EstimatorsNonparametric Estimation of QuantilesNonparametric Estimation of Intensities for Stochastic ProcessesEstimation in Semi-Parametric Regression ModelsDiffusion ProcessesApplications to Time Series Readership: Advanced undergraduate and graduate students in mathematical statistics and computational statistics; researchers in mathematical or applied statistics; statisticians. Keywords:Kernel Estimation;Density;Regression;Intensity;Diffusion;Nonparametric;Weak Convergence;Ergodic Process;Intensity of Point Process;Kernel Estimation;Single-Index Models;Functional Time Series;Variable BandwidthKey Features:Covers a wide range of nonparametric models and presents new functional estimatorsContains a detailed presentation of the mathematical techniques for functional estimation with recent advances in their optimizationA valuable resource for scientific researchers who model observation dataReviews: “This book is useful for researchers interested in the study of asymptotic properties of different types of optimum estimators obtained through the method of kernels.” Mathematical Reviews