Applications Of Non Parametric Density Estimation
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Applications Of Non Parametric Density Estimation
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Author : Ahmed Mohamed M. Sultan (LT COL, Egyptian AF.)
language : en
Publisher:
Release Date : 1990
Applications Of Non Parametric Density Estimation written by Ahmed Mohamed M. Sultan (LT COL, Egyptian AF.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Goodness-of-fit tests categories.
Applications Of Non Parametric Density Estimation
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Author : Ahmed Mohamed Mohamed Sultan
language : en
Publisher:
Release Date : 1990
Applications Of Non Parametric Density Estimation written by Ahmed Mohamed Mohamed Sultan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Density functionals categories.
The dissertation examines various methods of nonparametric density estimation, and nonparametric kernel estimation in more detail. The consequences of various kernel window width and their effect on the mean integrated square error are examined using Monte Carlo techniques. The mean and the variance of nonparametric density estimator is derived for symmetric kernels with finite mean and finite variance. The results also treat kernels with varying window parameters. The nonparametric kernel estimate was used to obtain new estimators for the three parameter Weibull distribution using distance estimation and the Cramer-von-Mises statistic. Comparison with maximum likelihood estimators using a Monte Carlo sample of size 1000 and various different parameters showed a significant improvement over the maximum likelihood estimators in the mean integrated square error between the estimated distribution and the true distribution. Several new goodness of fit tests are proposed using the nonparametric kernel estimator and the Cramer-von-Mises and the Anderson Darling statistics. Extensive Monte Carlo experiments were performed to obtain the critical values for the test and to study the power of the tests against eight alternative distributions. The tests using the Anderson Darling statistic showed greater power against almost all alternative distributions studied than the K.S. test. (kr).
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.
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.
Nonparametric Curve Estimation
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Author : Sam Efromovich
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-01-19
Nonparametric Curve Estimation written by Sam Efromovich 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 2008-01-19 with Mathematics categories.
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 Density Estimation Using Correlated Data With Application To Screening Problems
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Author : Junjun Qin
language : en
Publisher:
Release Date : 2013
Nonparametric Density Estimation Using Correlated Data With Application To Screening Problems written by Junjun Qin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
To detect a rare phenomenon with a higher probability by means of some other variable(s) which is (are) highly related but easier in technology or cheaper in expenditure is termed as "screening". It has come to be realized in recent years that current methodologies are of limited practical value. They are either built on a parametric framework which in fact we have little knowledge about or little involved with "multivariate screening" and "multi-stage screening", which are more frequent and more important in practice. Therefore, the alternative method discussed here suggests that we consider a nonparametric density estimation using correlated data. This one derives from and greatly formulates a rough idea that screening in nature is a kind of problems about how to solve probabilities and thus we intuitively look for estimating the probability density function (pdf). Since we have little information about data in a regular setting, nonparametric estimation seems a best candidate. In this study, the approach is presented by firstly making nonparametric density estimation in the case where random variables X and Y are both continuous and in the case where X is continuous but Y is discrete respectively. The behaviors under the large sample setting are explored and the performance of the proposed method is evaluated through simulations. The proposed method is then applied to a subsequent screening problem. The comparison with some current method is also presented.
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 Density Estimation With Applications
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Author : Steven J. Bean
language : en
Publisher:
Release Date : 1979
Nonparametric Density Estimation With Applications written by Steven J. Bean and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1979 with Nonparametric statistics categories.
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 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.
Density Estimation For Statistics And Data Analysis
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Author : Bernard. W. Silverman
language : en
Publisher: Routledge
Release Date : 2018-02-19
Density Estimation For Statistics And Data Analysis written by Bernard. W. Silverman and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-19 with Mathematics categories.
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.
