Nonparametric Density Estimation Using Correlated Data With Application To Screening Problems
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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.
Applied Smoothing Techniques For Data Analysis
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Author : Adrian W. Bowman
language : en
Publisher: OUP Oxford
Release Date : 1997-08-14
Applied Smoothing Techniques For Data Analysis written by Adrian W. Bowman and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-08-14 with Mathematics categories.
The book describes the use of smoothing techniques in statistics, including both density estimation and nonparametric regression. Considerable advances in research in this area have been made in recent years. The aim of this text is to describe a variety of ways in which these methods can be applied to practical problems in statistics. The role of smoothing techniques in exploring data graphically is emphasised, but the use of nonparametric curves in drawing conclusions from data, as an extension of more standard parametric models, is also a major focus of the book. Examples are drawn from a wide range of applications. The book is intended for those who seek an introduction to the area, with an emphasis on applications rather than on detailed theory. It is therefore expected that the book will benefit those attending courses at an advanced undergraduate, or postgraduate, level, as well as researchers, both from statistics and from other disciplines, who wish to learn about and apply these techniques in practical data analysis. The text makes extensive reference to S-Plus, as a computing environment in which examples can be explored. S-Plus functions and example scripts are provided to implement many of the techniques described. These parts are, however, clearly separate from the main body of text, and can therefore easily be skipped by readers not interested in S-Plus.
Deconvolution Problems In Nonparametric Statistics
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Author : Alexander Meister
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-12-24
Deconvolution Problems In Nonparametric Statistics written by Alexander Meister 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 2009-12-24 with Mathematics categories.
Deconvolution problems occur in many ?elds of nonparametric statistics, for example, density estimation based on contaminated data, nonparametric - gression with errors-in-variables, image and signal deblurring. During the last two decades, those topics have received more and more attention. As appli- tions of deconvolution procedures concern many real-life problems in eco- metrics, biometrics, medical statistics, image reconstruction, one can realize an increasing number of applied statisticians who are interested in nonpa- metric deconvolution methods; on the other hand, some deep results from Fourier analysis, functional analysis, and probability theory are required to understand the construction of deconvolution techniques and their properties so that deconvolution is also particularly challenging for mathematicians. Thegeneraldeconvolutionprobleminstatisticscanbedescribedasfollows: Our goal is estimating a function f while any empirical access is restricted to some quantity h = f?G = f(x?y)dG(y), (1. 1) that is, the convolution of f and some probability distribution G. Therefore, f can be estimated from some observations only indirectly. The strategy is ˆ estimating h ?rst; this means producing an empirical version h of h and, then, ˆ applying a deconvolution procedure to h to estimate f. In the mathematical context, we have to invert the convolution operator with G where some reg- ˆ ularization is required to guarantee that h is contained in the invertibility ˆ domain of the convolution operator. The estimator h has to be chosen with respect to the speci?c statistical experiment.
Nonparametric Statistics With Applications To Science And Engineering With R
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Author : Paul Kvam
language : en
Publisher: John Wiley & Sons
Release Date : 2022-10-18
Nonparametric Statistics With Applications To Science And Engineering With R written by Paul Kvam and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-18 with Mathematics categories.
NONPARAMETRIC STATISTICS WITH APPLICATIONS TO SCIENCE AND ENGINEERING WITH R Introduction to the methods and techniques of traditional and modern nonparametric statistics, incorporating R code Nonparametric Statistics with Applications to Science and Engineering with R presents modern nonparametric statistics from a practical point of view, with the newly revised edition including custom R functions implementing nonparametric methods to explain how to compute them and make them more comprehensible. Relevant built-in functions and packages on CRAN are also provided with a sample code. R codes in the new edition not only enable readers to perform nonparametric analysis easily, but also to visualize and explore data using R’s powerful graphic systems, such as ggplot2 package and R base graphic system. The new edition includes useful tables at the end of each chapter that help the reader find data sets, files, functions, and packages that are used and relevant to the respective chapter. New examples and exercises that enable readers to gain a deeper insight into nonparametric statistics and increase their comprehension are also included. Some of the sample topics discussed in Nonparametric Statistics with Applications to Science and Engineering with R include: Basics of probability, statistics, Bayesian statistics, order statistics, Kolmogorov–Smirnov test statistics, rank tests, and designed experiments Categorical data, estimating distribution functions, density estimation, least squares regression, curve fitting techniques, wavelets, and bootstrap sampling EM algorithms, statistical learning, nonparametric Bayes, WinBUGS, properties of ranks, and Spearman coefficient of rank correlation Chi-square and goodness-of-fit, contingency tables, Fisher exact test, MC Nemar test, Cochran’s test, Mantel–Haenszel test, and Empirical Likelihood Nonparametric Statistics with Applications to Science and Engineering with R is a highly valuable resource for graduate students in engineering and the physical and mathematical sciences, as well as researchers who need a more comprehensive, but succinct understanding of modern nonparametric statistical methods.
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.
Selected Topics In Nonparametric Testing And Variable Selection For High Dimensional Data
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Author : Pengsheng Ji
language : en
Publisher:
Release Date : 2012
Selected Topics In Nonparametric Testing And Variable Selection For High Dimensional Data written by Pengsheng Ji and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Statistical Modeling Using Local Gaussian Approximation
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Author : Dag Tjøstheim
language : en
Publisher: Academic Press
Release Date : 2021-10-05
Statistical Modeling Using Local Gaussian Approximation written by Dag Tjøstheim and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-05 with Business & Economics categories.
Statistical Modeling using Local Gaussian Approximation extends powerful characteristics of the Gaussian distribution, perhaps, the most well-known and most used distribution in statistics, to a large class of non-Gaussian and nonlinear situations through local approximation. This extension enables the reader to follow new methods in assessing dependence and conditional dependence, in estimating probability and spectral density functions, and in discrimination. Chapters in this release cover Parametric, nonparametric, locally parametric, Dependence, Local Gaussian correlation and dependence, Local Gaussian correlation and the copula, Applications in finance, and more. Additional chapters explores Measuring dependence and testing for independence, Time series dependence and spectral analysis, Multivariate density estimation, Conditional density estimation, The local Gaussian partial correlation, Regression and conditional regression quantiles, and a A local Gaussian Fisher discriminant. Reviews local dependence modeling with applications to time series and finance markets Introduces new techniques for density estimation, conditional density estimation, and tests of conditional independence with applications in economics Evaluates local spectral analysis, discovering hidden frequencies in extremes and hidden phase differences Integrates textual content with three useful R packages
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).
Statistical Analysis Techniques In Particle Physics
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Author : Ilya Narsky
language : en
Publisher: John Wiley & Sons
Release Date : 2013-10-24
Statistical Analysis Techniques In Particle Physics written by Ilya Narsky and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-24 with Science categories.
Modern analysis of HEP data needs advanced statistical tools to separate signal from background. This is the first book which focuses on machine learning techniques. It will be of interest to almost every high energy physicist, and, due to its coverage, suitable for students.
Nonparametric Kernel Density Estimation And Its Computational Aspects
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Author : Artur Gramacki
language : en
Publisher: Springer
Release Date : 2018-01-22
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 2018-01-22 with Computers 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.