Nonparametric Probability Density Estimation
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Nonparametric Probability Density Estimation
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Author : Richard A. Tapia
language : en
Publisher:
Release Date : 1978
Nonparametric Probability Density Estimation written by Richard A. Tapia and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1978 with Mathematics categories.
Nonparametric Density Estimation
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Author : Luc Devroye
language : en
Publisher: New York ; Toronto : Wiley
Release Date : 1985-01-18
Nonparametric Density Estimation written by Luc Devroye and has been published by New York ; Toronto : Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985-01-18 with Mathematics categories.
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.
Nonparametric Econometrics
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Author : Qi Li
language : en
Publisher: Princeton University Press
Release Date : 2011-10-09
Nonparametric Econometrics written by Qi Li and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-09 with Business & Economics categories.
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.
Nonparametric Kernel Density Estimation And Its Computational Aspects
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Author : Artur Gramacki
language : en
Publisher: Springer
Release Date : 2017-12-21
Nonparametric Kernel Density Estimation And Its Computational Aspects written by Artur Gramacki and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-21 with Technology & Engineering categories.
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.
A Non Parametric Probability Density Estimator And Some Applications
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Author : Ronald P. Fuchs
language : en
Publisher:
Release Date : 1984
A Non Parametric Probability Density Estimator And Some Applications written by Ronald P. Fuchs and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1984 with Density functionals categories.
In this thesis a new non-parametric probability density estimator is developed which has the following properties: (1) It yields a continuous, non-negative and piecewise linear estimate of a probability density function. (2) It converges to the true density function if the true density has no more than a finite number of discontinuities of a form where the value of the function at the discontinuity can be considered the average of the limiting values on either side of the discontinuity. (3) It requires no user supplied parameters. The estimator is shown to have significantly better error properties, for certain classes of distributions, than existing density estimators. The quality of the estimate is discussed, tabulated and graphically demonstrated. Applications, including parameterization, small sample analysis, and two sample tests are presented. These newly developed applications are shown to improve upon the generally accepted existing techniques. Guidelines for choosing a density estimation method along with an organized approach to method selection are discussed. Key words include: Statistical functions, Statistical tests, Nonparametric statistics, Probability density functions, Statistics.
Combinatorial Methods In Density Estimation
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Author : Luc Devroye
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Combinatorial Methods In Density Estimation written by Luc Devroye and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
Density estimation has evolved enormously since the days of bar plots and histograms, but researchers and users are still struggling with the problem of the selection of the bin widths. This text explores a new paradigm for the data-based or automatic selection of the free parameters of density estimates in general so that the expected error is within a given constant multiple of the best possible error. The paradigm can be used in nearly all density estimates and for most model selection problems, both parametric and nonparametric. It is the first book on this topic. The text is intended for first-year graduate students in statistics and learning theory, and offers a host of opportunities for further research and thesis topics. Each chapter corresponds roughly to one lecture, and is supplemented with many classroom exercises. A one year course in probability theory at the level of Feller's Volume 1 should be more than adequate preparation. Gabor Lugosi is Professor at Universitat Pompeu Fabra in Barcelona, and Luc Debroye is Professor at McGill University in Montreal. In 1996, the authors, together with Lászlo Györfi, published the successful text, A Probabilistic Theory of Pattern Recognition with Springer-Verlag. Both authors have made many contributions in the area of nonparametric estimation.
All Of Nonparametric Statistics
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Author : Larry Wasserman
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-09-10
All Of Nonparametric Statistics written by Larry Wasserman and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-09-10 with Mathematics categories.
There are many books on various aspects of nonparametric inference such as density estimation, nonparametric regression, bootstrapping, and wavelets methods. But it is hard to ?nd all these topics covered in one place. The goal of this text is to provide readers with a single book where they can ?nd a brief account of many of the modern topics in nonparametric inference. The book is aimed at master’s-level or Ph. D. -level statistics and computer science students. It is also suitable for researchersin statistics, machine lea- ing and data mining who want to get up to speed quickly on modern n- parametric methods. My goal is to quickly acquaint the reader with the basic concepts in many areas rather than tackling any one topic in great detail. In the interest of covering a wide range of topics, while keeping the book short, I have opted to omit most proofs. Bibliographic remarks point the reader to references that contain further details. Of course, I have had to choose topics to include andto omit,the title notwithstanding. For the mostpart,I decided to omit topics that are too big to cover in one chapter. For example, I do not cover classi?cation or nonparametric Bayesian inference. The book developed from my lecture notes for a half-semester (20 hours) course populated mainly by master’s-level students. For Ph. D.
Probability For Machine Learning
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2019-09-24
Probability For Machine Learning written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-24 with Computers categories.
Probability is the bedrock of machine learning. You cannot develop a deep understanding and application of machine learning without it. Cut through the equations, Greek letters, and confusion, and discover the topics in probability that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of probability to machine learning, Bayesian probability, entropy, density estimation, maximum likelihood, and much more.
Modern Multivariate Statistical Techniques
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Author : Alan J. Izenman
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-03-02
Modern Multivariate Statistical Techniques written by Alan J. Izenman and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-03-02 with Mathematics categories.
This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.
Nonparametric Function Estimation Modeling And Simulation
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Author : James R. Thompson
language : en
Publisher: SIAM
Release Date : 1990-01-01
Nonparametric Function Estimation Modeling And Simulation written by James R. Thompson and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990-01-01 with Mathematics categories.
Topics emphasized in this book include nonparametric density estimation, multi-dimensional data analysis, cancer progression, chaos theory, and parallel based algorithms.