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Convergence Rates In Density Estimation For Data From Infinite Order Moving Average Processes
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Book Synopsis Convergence Rates in Density Estimation for Data from Infinite-order Moving Average Processes by : Peter Hall
Download or read book Convergence Rates in Density Estimation for Data from Infinite-order Moving Average Processes written by Peter Hall and published by . This book was released on 1990 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: The effect of long-range dependence in nonparametric probability density estimation is investigated under the assumption that the observed data are a sample from a stationary, infinite-order moving average process. It is shown that to first order, the mean integrated squared error (MISE) of a kernel estimator for moving average data may be expanded as the sum of MISE of the kernel estimator for a same-size random sample, plus a term proportional to the variance of the moving average sample mean. The latter term does not depend on bandwidth, and so imposes a ceiling on the convergence rate of a kernel estimator regardless of how bandwidth is chosen. This ceiling can be quite significant in the case of long-range dependence. We show that all density estimators have the convergence rate ceiling possessed by kernel estimators.
Book Synopsis Convergence rates in density estimation for data from infinite-order moving average processes by :
Download or read book Convergence rates in density estimation for data from infinite-order moving average processes written by and published by . This book was released on 1989 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Long-Memory Processes by : Jan Beran
Download or read book Long-Memory Processes written by Jan Beran and published by Springer Science & Business Media. This book was released on 2013-05-14 with total page 892 pages. Available in PDF, EPUB and Kindle. Book excerpt: Long-memory processes are known to play an important part in many areas of science and technology, including physics, geophysics, hydrology, telecommunications, economics, finance, climatology, and network engineering. In the last 20 years enormous progress has been made in understanding the probabilistic foundations and statistical principles of such processes. This book provides a timely and comprehensive review, including a thorough discussion of mathematical and probabilistic foundations and statistical methods, emphasizing their practical motivation and mathematical justification. Proofs of the main theorems are provided and data examples illustrate practical aspects. This book will be a valuable resource for researchers and graduate students in statistics, mathematics, econometrics and other quantitative areas, as well as for practitioners and applied researchers who need to analyze data in which long memory, power laws, self-similar scaling or fractal properties are relevant.
Book Synopsis Advances in Econometrics: Volume 1 by : Christopher A. Sims
Download or read book Advances in Econometrics: Volume 1 written by Christopher A. Sims and published by Cambridge University Press. This book was released on 1996-03-07 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first of a two-volume set of articles reflecting the current state of research in econometrics.
Book Synopsis Smoothing Methods in Statistics by : Jeffrey S. Simonoff
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.
Book Synopsis Theory and Applications of Long-Range Dependence by : Paul Doukhan
Download or read book Theory and Applications of Long-Range Dependence written by Paul Doukhan and published by Springer Science & Business Media. This book was released on 2002-12-13 with total page 744 pages. Available in PDF, EPUB and Kindle. Book excerpt: The area of data analysis has been greatly affected by our computer age. For example, the issue of collecting and storing huge data sets has become quite simplified and has greatly affected such areas as finance and telecommunications. Even non-specialists try to analyze data sets and ask basic questions about their structure. One such question is whether one observes some type of invariance with respect to scale, a question that is closely related to the existence of long-range dependence in the data. This important topic of long-range dependence is the focus of this unique work, written by a number of specialists on the subject. The topics selected should give a good overview from the probabilistic and statistical perspective. Included will be articles on fractional Brownian motion, models, inequalities and limit theorems, periodic long-range dependence, parametric, semiparametric, and non-parametric estimation, long-memory stochastic volatility models, robust estimation, and prediction for long-range dependence sequences. For those graduate students and researchers who want to use the methodology and need to know the "tricks of the trade," there will be a special section called "Mathematical Techniques." Topics in the first part of the book are covered from probabilistic and statistical perspectives and include fractional Brownian motion, models, inequalities and limit theorems, periodic long-range dependence, parametric, semiparametric, and non-parametric estimation, long-memory stochastic volatility models, robust estimation, prediction for long-range dependence sequences. The reader is referred to more detailed proofs if already found in the literature. The last part of the book is devoted to applications in the areas of simulation, estimation and wavelet techniques, traffic in computer networks, econometry and finance, multifractal models, and hydrology. Diagrams and illustrations enhance the presentation. Each article begins with introductory background material and is accessible to mathematicians, a variety of practitioners, and graduate students. The work serves as a state-of-the art reference or graduate seminar text.
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 227 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 facilita
Book Synopsis Statistical Theory and Method Abstracts by :
Download or read book Statistical Theory and Method Abstracts written by and published by . This book was released on 1998 with total page 670 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Journal of Statistical Planning and Inference by :
Download or read book Journal of Statistical Planning and Inference written by and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis INTERNATIONAL JOURNAL OF FORECASTING by : JAN G. DE GOOIJER
Download or read book INTERNATIONAL JOURNAL OF FORECASTING written by JAN G. DE GOOIJER and published by . This book was released on 2002 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Annals of Statistics written by and published by . This book was released on 2007 with total page 912 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Studies in Time Series and Random Dynamics by : Wei Biao Wu
Download or read book Studies in Time Series and Random Dynamics written by Wei Biao Wu and published by . This book was released on 2001 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Effective Implementations of Nonparametric Nonlinear Time Series Analysis by : Min-Jay Wang
Download or read book Effective Implementations of Nonparametric Nonlinear Time Series Analysis written by Min-Jay Wang and published by . This book was released on 2000 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bulletin de L'Institut International de Statistique by :
Download or read book Bulletin de L'Institut International de Statistique written by and published by . This book was released on 1995 with total page 928 pages. Available in PDF, EPUB and Kindle. Book excerpt: V. 1-5, v. 7-10 include "Bulletin bibliographique."
Book Synopsis Acta Scientiarum Mathematicarum by : József Attila Tudományegyetem
Download or read book Acta Scientiarum Mathematicarum written by József Attila Tudományegyetem and published by . This book was released on 1995 with total page 838 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Actes de la Session by : International Statistical Institute
Download or read book Actes de la Session written by International Statistical Institute and published by . This book was released on 1995 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Large Covariance and Autocovariance Matrices by : Arup Bose
Download or read book Large Covariance and Autocovariance Matrices written by Arup Bose and published by CRC Press. This book was released on 2018-07-03 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large Covariance and Autocovariance Matrices brings together a collection of recent results on sample covariance and autocovariance matrices in high-dimensional models and novel ideas on how to use them for statistical inference in one or more high-dimensional time series models. The prerequisites include knowledge of elementary multivariate analysis, basic time series analysis and basic results in stochastic convergence. Part I is on different methods of estimation of large covariance matrices and auto-covariance matrices and properties of these estimators. Part II covers the relevant material on random matrix theory and non-commutative probability. Part III provides results on limit spectra and asymptotic normality of traces of symmetric matrix polynomial functions of sample auto-covariance matrices in high-dimensional linear time series models. These are used to develop graphical and significance tests for different hypotheses involving one or more independent high-dimensional linear time series. The book should be of interest to people in econometrics and statistics (large covariance matrices and high-dimensional time series), mathematics (random matrices and free probability) and computer science (wireless communication). Parts of it can be used in post-graduate courses on high-dimensional statistical inference, high-dimensional random matrices and high-dimensional time series models. It should be particularly attractive to researchers developing statistical methods in high-dimensional time series models. Arup Bose is a professor at the Indian Statistical Institute, Kolkata, India. He is a distinguished researcher in mathematical statistics and has been working in high-dimensional random matrices for the last fifteen years. He has been editor of Sankhyā for several years and has been on the editorial board of several other journals. He is a Fellow of the Institute of Mathematical Statistics, USA and all three national science academies of India, as well as the recipient of the S.S. Bhatnagar Award and the C.R. Rao Award. His first book Patterned Random Matrices was also published by Chapman & Hall. He has a forthcoming graduate text U-statistics, M-estimates and Resampling (with Snigdhansu Chatterjee) to be published by Hindustan Book Agency. Monika Bhattacharjee is a post-doctoral fellow at the Informatics Institute, University of Florida. After graduating from St. Xavier's College, Kolkata, she obtained her master’s in 2012 and PhD in 2016 from the Indian Statistical Institute. Her thesis in high-dimensional covariance and auto-covariance matrices, written under the supervision of Dr. Bose, has received high acclaim.
