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Author : Michael H. Neumann
Publisher :
ISBN 13 :
Total Pages : 26 pages
Book Rating : 4.:/5 (632 download)
Download or read book On the Effect of Estimating the Error Density in Nonparametric Deconvolution written by Michael H. Neumann and published by . This book was released on 1995 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Alexander Meister
Publisher : Springer Science & Business Media
ISBN 13 : 3540875573
Total Pages : 211 pages
Book Rating : 4.5/5 (48 download)
Download or read book Deconvolution Problems in Nonparametric Statistics written by Alexander Meister and published by Springer Science & Business Media. This book was released on 2009-12-24 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Grace Y. Yi
Publisher : CRC Press
ISBN 13 : 1351588591
Total Pages : 648 pages
Book Rating : 4.3/5 (515 download)
Download or read book Handbook of Measurement Error Models written by Grace Y. Yi and published by CRC Press. This book was released on 2021-09-28 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: Measurement error arises ubiquitously in applications and has been of long-standing concern in a variety of fields, including medical research, epidemiological studies, economics, environmental studies, and survey research. While several research monographs are available to summarize methods and strategies of handling different measurement error problems, research in this area continues to attract extensive attention. The Handbook of Measurement Error Models provides overviews of various topics on measurement error problems. It collects carefully edited chapters concerning issues of measurement error and evolving statistical methods, with a good balance of methodology and applications. It is prepared for readers who wish to start research and gain insights into challenges, methods, and applications related to error-prone data. It also serves as a reference text on statistical methods and applications pertinent to measurement error models, for researchers and data analysts alike. Features: Provides an account of past development and modern advancement concerning measurement error problems Highlights the challenges induced by error-contaminated data Introduces off-the-shelf methods for mitigating deleterious impacts of measurement error Describes state-of-the-art strategies for conducting in-depth research
Author : Denis Belomestny
Publisher : Springer
ISBN 13 : 3319123734
Total Pages : 303 pages
Book Rating : 4.3/5 (191 download)
Download or read book Lévy Matters IV written by Denis Belomestny and published by Springer. This book was released on 2014-12-05 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this volume is to provide an extensive account of the most recent advances in statistics for discretely observed Lévy processes. These days, statistics for stochastic processes is a lively topic, driven by the needs of various fields of application, such as finance, the biosciences, and telecommunication. The three chapters of this volume are completely dedicated to the estimation of Lévy processes, and are written by experts in the field. The first chapter by Denis Belomestny and Markus Reiß treats the low frequency situation, and estimation methods are based on the empirical characteristic function. The second chapter by Fabienne Comte and Valery Genon-Catalon is dedicated to non-parametric estimation mainly covering the high-frequency data case. A distinctive feature of this part is the construction of adaptive estimators, based on deconvolution or projection or kernel methods. The last chapter by Hiroki Masuda considers the parametric situation. The chapters cover the main aspects of the estimation of discretely observed Lévy processes, when the observation scheme is regular, from an up-to-date viewpoint.
Author : Marie Davidian
Publisher : Springer
ISBN 13 : 3319058010
Total Pages : 599 pages
Book Rating : 4.3/5 (19 download)
Download or read book The Work of Raymond J. Carroll written by Marie Davidian and published by Springer. This book was released on 2014-06-06 with total page 599 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains Raymond J. Carroll's research and commentary on its impact by leading statisticians. Each of the seven main parts focuses on a key research area: Measurement Error, Transformation and Weighting, Epidemiology, Nonparametric and Semiparametric Regression for Independent Data, Nonparametric and Semiparametric Regression for Dependent Data, Robustness, and other work. The seven subject areas reviewed in this book were chosen by Ray himself, as were the articles representing each area. The commentaries not only review Ray’s work, but are also filled with history and anecdotes. Raymond J. Carroll’s impact on statistics and numerous other fields of science is far-reaching. His vast catalog of work spans from fundamental contributions to statistical theory to innovative methodological development and new insights in disciplinary science. From the outset of his career, rather than taking the “safe” route of pursuing incremental advances, Ray has focused on tackling the most important challenges. In doing so, it is fair to say that he has defined a host of statistics areas, including weighting and transformation in regression, measurement error modeling, quantitative methods for nutritional epidemiology and non- and semiparametric regression.
Author : Fuxia Cheng
Publisher :
ISBN 13 :
Total Pages : 134 pages
Book Rating : 4.3/5 (129 download)
Download or read book Error Density and Distribution Function Estimation in Nonparametric Regression Models written by Fuxia Cheng and published by . This book was released on 2002 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :
Publisher :
ISBN 13 :
Total Pages : 896 pages
Book Rating : 4.3/5 (91 download)
Download or read book Journal of the American Statistical Association written by and published by . This book was released on 2009 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Ming-Chung Liu
Publisher :
ISBN 13 :
Total Pages : 222 pages
Book Rating : 4.:/5 (174 download)
Download or read book Nonparametric Density Function Estimation and the Deconvolution Problem written by Ming-Chung Liu and published by . This book was released on 1987 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : N. H. Bingham
Publisher : Cambridge University Press
ISBN 13 : 1139487922
Total Pages : 547 pages
Book Rating : 4.1/5 (394 download)
Download or read book Probability and Mathematical Genetics written by N. H. Bingham and published by Cambridge University Press. This book was released on 2010-07-15 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: No leading university department of mathematics or statistics, or library, can afford to be without this unique text. Leading authorities give a unique insight into a wide range of currently topical problems, from the mathematics of road networks to the genomics of cancer.
Author :
Publisher :
ISBN 13 :
Total Pages : 408 pages
Book Rating : 4.3/5 (91 download)
Download or read book Mathematical Methods of Statistics written by and published by . This book was released on 2007 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : A. J. van Es
Publisher :
ISBN 13 :
Total Pages : 158 pages
Book Rating : 4.3/5 (91 download)
Download or read book Aspects of Nonparametric Density Estimation written by A. J. van Es and published by . This book was released on 1991 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Gwennaelle Mabon
Publisher :
ISBN 13 :
Total Pages : 23 pages
Book Rating : 4.:/5 (931 download)
Download or read book Adaptive Density Estimation in Deconvolution Problems with Unknown Error Distribution written by Gwennaelle Mabon and published by . This book was released on 2013 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Sam Efromovich
Publisher : CRC Press
ISBN 13 : 1351679848
Total Pages : 448 pages
Book Rating : 4.3/5 (516 download)
Download or read book Missing and Modified Data in Nonparametric Estimation written by Sam Efromovich and published by CRC Press. This book was released on 2018-03-12 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a systematic and unified approach for modern nonparametric treatment of missing and modified data via examples of density and hazard rate estimation, nonparametric regression, filtering signals, and time series analysis. All basic types of missing at random and not at random, biasing, truncation, censoring, and measurement errors are discussed, and their treatment is explained. Ten chapters of the book cover basic cases of direct data, biased data, nondestructive and destructive missing, survival data modified by truncation and censoring, missing survival data, stationary and nonstationary time series and processes, and ill-posed modifications. The coverage is suitable for self-study or a one-semester course for graduate students with a prerequisite of a standard course in introductory probability. Exercises of various levels of difficulty will be helpful for the instructor and self-study. The book is primarily about practically important small samples. It explains when consistent estimation is possible, and why in some cases missing data should be ignored and why others must be considered. If missing or data modification makes consistent estimation impossible, then the author explains what type of action is needed to restore the lost information. The book contains more than a hundred figures with simulated data that explain virtually every setting, claim, and development. The companion R software package allows the reader to verify, reproduce and modify every simulation and used estimators. This makes the material fully transparent and allows one to study it interactively. Sam Efromovich is the Endowed Professor of Mathematical Sciences and the Head of the Actuarial Program at the University of Texas at Dallas. He is well known for his work on the theory and application of nonparametric curve estimation and is the author of Nonparametric Curve Estimation: Methods, Theory, and Applications. Professor Sam Efromovich is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association.
Author :
Publisher :
ISBN 13 :
Total Pages : 516 pages
Book Rating : 4.3/5 (91 download)
Download or read book Statistica Sinica written by and published by . This book was released on 2009 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Luc Devroye
Publisher : New York ; Toronto : Wiley
ISBN 13 :
Total Pages : 376 pages
Book Rating : 4.3/5 (91 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 :
Publisher :
ISBN 13 :
Total Pages : 750 pages
Book Rating : 4.:/5 (7 download)
Download or read book Statistical Theory and Method Abstracts written by and published by . This book was released on 2001 with total page 750 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Linruo Guo
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)
Download or read book Estimating Multivariate Density Function of Mixed Measurement Error Data written by Linruo Guo and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Density estimation has been a long frontline research area in nonparametric smoothing. However, real applications oftentimes see the data contaminated with different types of measurement errors. Further data analysis, therefore, should take care of these errors to have a reliable statistical inference procedure. In this proposal, nonparametric density estimation for the data contaminated super-smooth, ordinary-smooth, Berkson measurement errors will be thoroughly investigated. Classical kernel and deconvolution kernel smoothing are used as building blocks to construct the estimators. In the first part, we propose a nonparametric mixed kernel estimator for a multivariate density function and its derivatives when the data are contaminated with different sources of measurement errors. The proposed estimator is a mixture of the classical and the deconvolution kernels, accounting for the error-free and error- prone variables, respectively. Large sample properties of the proposed nonparametric estimator, including the order of the mean squares error, the consistency, and the asymptotic normality, are discussed. The optimal convergence rates among all nonparametric estimators for different measurement error structures are derived, and it is shown that the proposed mixed kernel estimators achieve the optimal convergence rate. A simulation study is conducted to evaluate the finite sample performance of the proposed estimators. In the second part, we consider the nonparametric estimation for the joint density function of two random variables, when one variable is contaminated with Berkson measurement error, and another variable can be observed directly. Two estimators are proposed with or without applying the kernel smoothing for the data with Berkson measurement error. Mean squared errors are calculated for both estimators. Large sample properties, including weak consistencies, strong consistencies, uniform strong consistencies in probability, and asymptotic normality are derived. In addition, we develop a method for bandwidth selection in the kernel estimate of the probability density using the least squares cross-validation method. The performance of this method is further assessed by a simulation study.
