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Author : Bert van Es
Publisher :
ISBN 13 :
Total Pages : 25 pages
Book Rating : 4.:/5 (461 download)
Download or read book Likelihood Cross-validation Bandwidth Selection for Nonparametric Kernel Density Estimators written by Bert van Es and published by . This book was released on 1989 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Albertus Jacob Es
Publisher :
ISBN 13 :
Total Pages : 25 pages
Book Rating : 4.:/5 (648 download)
Download or read book Likelihood Cross-validation Bandwidth Selection for Nonparametric Kernel Density Estimators written by Albertus Jacob Es and published by . This book was released on 1989 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : M.P. Wand
Publisher : CRC Press
ISBN 13 : 9780412552700
Total Pages : 230 pages
Book Rating : 4.5/5 (527 download)
Download or read book Kernel Smoothing written by M.P. Wand and published by CRC Press. This book was released on 1994-12-01 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Qi Li
Publisher : Princeton University Press
ISBN 13 : 1400841062
Total Pages : 769 pages
Book Rating : 4.4/5 (8 download)
Download or read book Nonparametric Econometrics written by Qi Li and published by Princeton University Press. This book was released on 2011-10-09 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Artur Gramacki
Publisher : Springer
ISBN 13 : 3319716883
Total Pages : 197 pages
Book Rating : 4.3/5 (197 download)
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 197 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.
Author : Nils-Bastian Heidenreich
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Bandwidth Selection Methods for Kernel Density Estimation - A Review of Performance written by Nils-Bastian Heidenreich and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: On the one hand, kernel density estimation is a common tool for empirical studies in any research area. This goes hand in hand with the fact that these estimators are provided by many software packages. On the other hand, since about three decades the discussion on bandwidth selection has been going on. A good part of the discussion is concerned about nonparametric regression, but this issue is by no means less problematic for density estimation. This becomes obvious when reading empirical studies in which practitioners made use of kernel densities. Unfortunately, software packages offer only simple cross validation or Silverman's rule of thumb. New contributions typically provide simulations limited to show that the own invention outperforms existing methods. We review existing methods and compare them on a set of designs that exhibits features like few bumps and exponentially falling tails concentrating thereby on small and moderate sample sizes. Our main focus is on practical issues like fully automatic procedures, implementation and performance where the latter one is measured in many ways.
Author : J. S. Marron
Publisher :
ISBN 13 :
Total Pages : 27 pages
Book Rating : 4.:/5 (227 download)
Download or read book Asymptotically Optimal Bandwidth Selection for Kernel Density Estimators from Randomly Right-Censored Samples written by J. S. Marron and published by . This book was released on 1986 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper makes two important contributions to the theory of bandwidth selection for kernel density estimators under right censorship. First, an asymptotic representation of the integrated squared error into easily understood variance and squared bias components is given. Second, it is shown that if the bandwidth is chosen by the data-based method of least squares cross-validation, then it is asymptotically optimal in a compelling sense. A by-product of the first part is an interesting comparison of the two most popular kernel estimators. Keywords: Nonparametric density estimation; Smoothing parameter.
Author : Wolfgang Karl Härdle
Publisher : Springer Science & Business Media
ISBN 13 : 364217146X
Total Pages : 317 pages
Book Rating : 4.6/5 (421 download)
Download or read book Nonparametric and Semiparametric Models written by Wolfgang Karl Härdle and published by Springer Science & Business Media. This book was released on 2012-08-27 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers. The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity.
Author : Alina A. von Davier
Publisher : Springer Science & Business Media
ISBN 13 : 0387217193
Total Pages : 244 pages
Book Rating : 4.3/5 (872 download)
Download or read book The Kernel Method of Test Equating written by Alina A. von Davier and published by Springer Science & Business Media. This book was released on 2006-05-10 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: KE is applied to the four major equating designs and to both Chain Equating and Post-Stratification Equating for the Non-Equivalent groups with Anchor Test Design. It will be an important reference for several groups: (a) Statisticians (b) Practitioners and (c) Instructors in psychometric and measurement programs. The authors assume some familiarity with linear and equipercentile test equating, and with matrix algebra.
Author : Bernard. W. Silverman
Publisher : CRC Press
ISBN 13 : 9780412246203
Total Pages : 190 pages
Book Rating : 4.2/5 (462 download)
Download or read book Density Estimation for Statistics and Data Analysis written by Bernard. W. Silverman and published by CRC Press. This book was released on 1986-04-01 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Jeffrey S. Simonoff
Publisher : Springer Science & Business Media
ISBN 13 : 1461240263
Total Pages : 349 pages
Book Rating : 4.4/5 (612 download)
Download or read book Smoothing Methods in Statistics written by Jeffrey S. Simonoff and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Chi-Yang Chu
Publisher :
ISBN 13 :
Total Pages : 65 pages
Book Rating : 4.:/5 (999 download)
Download or read book Applied Nonparametric Density and Regression Estimation with Discrete Data written by Chi-Yang Chu and published by . This book was released on 2017 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bandwidth selection plays an important role in kernel density estimation. Least-squares cross-validation and plug-in methods are commonly used as bandwidth selectors for the continuous data setting. The former is a data-driven approach and the latter requires a priori assumptions about the unknown distribution of the data. A benefit from the plug-in method is its relatively quick computation and hence it is often used for preliminary analysis. However, we find that much less is known about the plug-in method in the discrete data setting and this motivates us to propose a plug-in bandwidth selector. A related issue is undersmoothing in kernel density estimation. Least-squares cross-validation is a popular bandwidth selector, but in many applied situations, it tends to select a relatively small bandwidth, or undersmooths. The literature suggests several methods to solve this problem, but most of them are the modifications of extant error criterions for continuous variables. Here we discuss this problem in the discrete data setting and propose non-geometric discrete kernel functions as a possible solution. This issue also occurs in kernel regression estimation. Our proposed bandwidth selector and kernel functions perform well in simulated and real data.
Author : José E. Chacón
Publisher : CRC Press
ISBN 13 : 0429939132
Total Pages : 255 pages
Book Rating : 4.4/5 (299 download)
Download or read book Multivariate Kernel Smoothing and Its Applications written by José E. Chacón and published by CRC Press. This book was released on 2018-05-08 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Tingting Cheng
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Semiparametric Localized Bandwidth Selection in Kernel Density Estimation written by Tingting Cheng and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since conventional cross-validation bandwidth selection methods don't work for the case where the data considered are dependent time series, alternative bandwidth selection methods are needed. In recent years, Bayesian based global bandwidth selection methods have been proposed. Our experience shows that the use of a global bandwidth is however less suitable than using a localized bandwidth in kernel density estimation in the case where the data are dependent time series as discussed in an empirical application of this paper. Nonetheless, a difficult issue is how we can consistently estimate a localized bandwidth. In this paper, we propose a semiparametric estimation method, for which we establish an asymptotic theory for the proposed semiparametric estimator. A by-product of this bandwidth estimate is a new sampling-based likelihood approach to hyperparameter estimation. Monte Carlo simulation studies show that the proposed hyperparameter estimation method works very well, and that the proposed bandwidth estimator outperforms its competitors. Applications of the new bandwidth estimator to the kernel density estimation of Eurodollar deposit rate, as well as the S&P 500 daily return under conditional heteroscedasticity, demonstrate the effectiveness and competitiveness of the proposed semiparametric localized bandwidth.
Author : Luc Devroye
Publisher : New York ; Toronto : Wiley
ISBN 13 :
Total Pages : 376 pages
Book Rating : 4.:/5 (319 download)
Download or read book Nonparametric Density Estimation written by Luc Devroye and published by New York ; Toronto : Wiley. This book was released on 1985-01-18 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Jeffrey D. Hart
Publisher :
ISBN 13 :
Total Pages : 44 pages
Book Rating : 4.:/5 (13 download)
Download or read book Data-driven Bandwidth Choice for Density Estimation Based on Dependent Data written by Jeffrey D. Hart and published by . This book was released on 1988 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: The bandwidth selection problem in kernel density estimation is investigated in situations where the observed data are dependent. The classical leave-out technique is extended, and thereby a class of cross-validated bandwidths is defined. These bandwidths are shown to be asymptotically optimal under a strong mixing condition. The leave-one out, or ordinary, form of cross-validation remains asymptotically optimal under the dependence model considered. However, a simulation study shows that when the data are strongly enough correlated, the ordinary version of cross-validation can be improved upon in finite-sized samples.
Author : Jake VanderPlas
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491912138
Total Pages : 743 pages
Book Rating : 4.4/5 (919 download)
Download or read book Python Data Science Handbook written by Jake VanderPlas and published by "O'Reilly Media, Inc.". This book was released on 2016-11-21 with total page 743 pages. Available in PDF, EPUB and Kindle. Book excerpt: For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
