Download or read book Robust prediction operations for stationary processes written by P. P. Kazakos and published by . This book was released on 1987 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers prediction for stationary processes, in environments where data outliers may be present. The develops a sequence of outlier resistant prediction operations, which is 'uniformly qualitatively robust. Studied are the asymptotic mean-squared performance of the developed operations, both in the absence and the presence of i.i.d. data outliers. Important performance characteristics studied include the breakdown point and the influence function. (Author).
Download or read book Robust prediction for stationary processes written by P. Papantoni-Kazakos and published by . This book was released on 1987 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Consider prediction for stationary processes, in environments where data outliers may be present. Two sequences are developed for outliers resistant prediction operations, which are uniformly qualitatively robust. The asymptotic mean-squared performance of the developed operations are studied in the absence of data outliers. Important performance characteristics studied include the breakdown point and the influence function. Included are numerical results, for some autoregressive nominal processes.
Download or read book Robust Prediction for Stationary Processes and Robust Predictive Coding for Gaussian Sources written by Kailash Birmiwal and published by . This book was released on 1988 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Prediction and Interpolation for Vector Stationary Processes written by Haralampos Tsaknakis and published by . This book was released on 1982 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: Asymptotic linear prediction and interpolation, for statistically contaminated vector stationary processes is considered. Both prediction and interpolation are then formalized as stochastic games with saddle point solutions. The existence of unique solutions on convex and closed classes of vector stationary processes is shown. Then, those solutions are found and analyzed, for two specific classes of vector stationary processes. (Author).
Download or read book Robust Prediction and Interpolation for Vector Stationary Processes written by H. Tsaknakis and published by . This book was released on 1984 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust multivariate prediction and interpolation problems for statistically contaminated vector valued second order stationary processes are considered. The statistical contamination is modeled by requiring that the spectra of the processes lie within certain non-parametric classes. Both prediction and interpolation are then formalized as games whose saddle point solutions are sought. Finally, such solutions are found and analyzed, for two specific multivariate spectral classes. (Author).
Download or read book Robust Prediction and Interpolation for Vector Stationary Processes 2d Enriched Version written by P. Papantoni-Kazakos and published by . This book was released on 1987 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main objectives of this research have been the development of smooth nonparametric estimators of quantile functions from right-censored data and the further study of smooth density estimators from censored observations. In particular, kernel type and generalized quantile estimators have been obtained under censoring which give better estimates of percentiles of the lifetime distribution than the usual product-limit quantile estimator. Other new results include the study of linear empirical Bayes estimators, prediction intervals for the inverse Gaussian distribution, nonparametric hazard rate estimation under censoring, nonparametric inference for step-stress accelerated life tests under censoring. Discrete failure models, reliability estimation when cause of failure is partially known, Gompertzian failure models, simultaneous confidence intervals for pairwise differences of normal means, and optimal designs for comparing treatments with a control.
Download or read book Stationary Processes and Prediction Theory written by Harry Furstenberg and published by Princeton University Press. This book was released on 1960-08-21 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: A classic treatment of stationary processes and prediction theory from the acclaimed Annals of Mathematics Studies series Princeton University Press is proud to have published the Annals of Mathematics Studies since 1940. One of the oldest and most respected series in science publishing, it has included many of the most important and influential mathematical works of the twentieth century. The series continues this tradition as Princeton University Press publishes the major works of the twenty-first century. To mark the continued success of the series, all books are available in paperback and as ebooks.
Download or read book Empirical Likelihood and Quantile Methods for Time Series written by Yan Liu and published by Springer. This book was released on 2018-12-05 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book integrates the fundamentals of asymptotic theory of statistical inference for time series under nonstandard settings, e.g., infinite variance processes, not only from the point of view of efficiency but also from that of robustness and optimality by minimizing prediction error. This is the first book to consider the generalized empirical likelihood applied to time series models in frequency domain and also the estimation motivated by minimizing quantile prediction error without assumption of true model. It provides the reader with a new horizon for understanding the prediction problem that occurs in time series modeling and a contemporary approach of hypothesis testing by the generalized empirical likelihood method. Nonparametric aspects of the methods proposed in this book also satisfactorily address economic and financial problems without imposing redundantly strong restrictions on the model, which has been true until now. Dealing with infinite variance processes makes analysis of economic and financial data more accurate under the existing results from the demonstrative research. The scope of applications, however, is expected to apply to much broader academic fields. The methods are also sufficiently flexible in that they represent an advanced and unified development of prediction form including multiple-point extrapolation, interpolation, and other incomplete past forecastings. Consequently, they lead readers to a good combination of efficient and robust estimate and test, and discriminate pivotal quantities contained in realistic time series models.
Download or read book Estimation of Stochastic Processes with Stationary Increments and Cointegrated Sequences written by Maksym Luz and published by John Wiley & Sons. This book was released on 2019-12-12 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimation of Stochastic Processes is intended for researchers in the field of econometrics, financial mathematics, statistics or signal processing. This book gives a deep understanding of spectral theory and estimation techniques for stochastic processes with stationary increments. It focuses on the estimation of functionals of unobserved values for stochastic processes with stationary increments, including ARIMA processes, seasonal time series and a class of cointegrated sequences. Furthermore, this book presents solutions to extrapolation (forecast), interpolation (missed values estimation) and filtering (smoothing) problems based on observations with and without noise, in discrete and continuous time domains. Extending the classical approach applied when the spectral densities of the processes are known, the minimax method of estimation is developed for a case where the spectral information is incomplete and the relations that determine the least favorable spectral densities for the optimal estimations are found.
Download or read book Banach space valued Stationary Processes and Their Linear Prediction written by S. A. Chobanyan and published by . This book was released on 1975 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1988 with total page 974 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Technical Reports Awareness Circular TRAC written by and published by . This book was released on 1987-12 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Statistics written by Ricardo A. Maronna and published by John Wiley & Sons. This book was released on 2018-10-19 with total page 533 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new edition of this popular text on robust statistics, thoroughly updated to include new and improved methods and focus on implementation of methodology using the increasingly popular open-source software R. Classical statistics fail to cope well with outliers associated with deviations from standard distributions. Robust statistical methods take into account these deviations when estimating the parameters of parametric models, thus increasing the reliability of fitted models and associated inference. This new, second edition of Robust Statistics: Theory and Methods (with R) presents a broad coverage of the theory of robust statistics that is integrated with computing methods and applications. Updated to include important new research results of the last decade and focus on the use of the popular software package R, it features in-depth coverage of the key methodology, including regression, multivariate analysis, and time series modeling. The book is illustrated throughout by a range of examples and applications that are supported by a companion website featuring data sets and R code that allow the reader to reproduce the examples given in the book. Unlike other books on the market, Robust Statistics: Theory and Methods (with R) offers the most comprehensive, definitive, and up-to-date treatment of the subject. It features chapters on estimating location and scale; measuring robustness; linear regression with fixed and with random predictors; multivariate analysis; generalized linear models; time series; numerical algorithms; and asymptotic theory of M-estimates. Explains both the use and theoretical justification of robust methods Guides readers in selecting and using the most appropriate robust methods for their problems Features computational algorithms for the core methods Robust statistics research results of the last decade included in this 2nd edition include: fast deterministic robust regression, finite-sample robustness, robust regularized regression, robust location and scatter estimation with missing data, robust estimation with independent outliers in variables, and robust mixed linear models. Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is an ideal resource for researchers, practitioners, and graduate students in statistics, engineering, computer science, and physical and social sciences.
Download or read book Modelling Robustness and Sensitivity Reduction in Control Systems written by Ruth F. Curtain and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: Historically, one of the basic issues in control systems design has been robustness: the ability of a controlled plant to withstand variations in or lack of knowledge of its dynamics. Even if the dynamics of a system are accurately known for purposes of implementation, it is often desirable to design a control system based on a simplified model. Consequently it is essential to be able to guarantee a reasonable performance not only for the nominal plant, but also for its neighbouring perturbations: this is the issue of robustness. Since the beginning of this decade major advances have been made in this area, notably using the H -approach; this term is meant to cover the solution of sensitivity reduction, approximation and model reduction, robustness and related control design problems using the mathematics of Hardy spaces and related areas in Harmonic Analysis. This book contains the proceedings of the NATO Advanced Research Workshop on "Modelling, Robustness and Sensitivity Reduction in Control Systems", which was held at the University of Groningen, December 1986. Its aim was to explore the development of H -design techniques and its ramifications in Systems Theory in a unified and systematic way with the emphasis on recent advances and future directions in this fast developing area. In particular the following inter-related aspects were addressed: H -mathematical foundations, model approximation and robustness in control design, optimal sensitivity reduction, modelling and system identification and signal processing.
Download or read book Introduction to Time Series and Forecasting written by Peter J. Brockwell and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: Some of the key mathematical results are stated without proof in order to make the underlying theory acccessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The emphasis is on methods and the analysis of data sets. The logic and tools of model-building for stationary and non-stationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introducitons are also given to cointegration and to non-linear, continuous-time and long-memory models. The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, by Springer-Verlag, 1994. It does not handle such large data sets as ITSM for Windows, but like the latter, runs on IBM-PC compatible computers under either DOS or Windows (version 3.1 or later). The programs are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis.
Download or read book Robust and Nonlinear Time Series Analysis written by J. Franke and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. The, most commonly used assumption is that the data is a realization of a stationary Gaussian process. However, since the Gaussian assumption is a fairly stringent one, this assumption is frequently replaced by the weaker assumption that the process is wide~sense stationary and that only the mean and covariance sequence is specified. This approach of specifying the probabilistic behavior only up to "second order" has of course been extremely popular from a theoretical point of view be cause it has allowed one to treat a large variety of problems, such as prediction, filtering and smoothing, using the geometry of Hilbert spaces. While the literature abounds with a variety of optimal estimation results based on either the Gaussian assumption or the specification of second-order properties, time series workers have not always believed in the literal truth of either the Gaussian or second-order specifica tion. They have none-the-less stressed the importance of such optimali ty results, probably for two main reasons: First, the results come from a rich and very workable theory. Second, the researchers often relied on a vague belief in a kind of continuity principle according to which the results of time series inference would change only a small amount if the actual model deviated only a small amount from the assum ed model.
Download or read book Use of Services for Family Planning and Infertility United States 1982 written by Gerry E. Hendershot and published by . This book was released on 1988 with total page 982 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 1982 statistics on the use of family planning and infertility services presented in this report are preliminary results from Cycle III of the National Survey of Family Growth (NSFG), conducted by the National Center for Health Statistics. Data were collected through personal interviews with a multistage area probability sample of 7969 women aged 15-44. A detailed series of questions was asked to obtain relatively complete estimates of the extent and type of family planning services received. Statistics on family planning services are limited to women who were able to conceive 3 years before the interview date. Overall, 79% of currently mrried nonsterile women reported using some type of family planning service during the previous 3 years. There were no statistically significant differences between white (79%), black (75%) or Hispanic (77%) wives, or between the 2 income groups. The 1982 survey questions were more comprehensive than those of earlier cycles of the survey. The annual rate of visits for family planning services in 1982 was 1077 visits /1000 women. Teenagers had the highest annual visit rate (1581/1000) of any age group for all sources of family planning services combined. Visit rates declined sharply with age from 1447 at ages 15-24 to 479 at ages 35-44. Similar declines with age also were found in the visit rates for white and black women separately. Nevertheless, the annual visit rate for black women (1334/1000) was significantly higher than that for white women (1033). The highest overall visit rate was for black women 15-19 years of age (1867/1000). Nearly 2/3 of all family planning visits were to private medical sources. Teenagers of all races had higher family planning service visit rates to clinics than to private medical sources, as did black women age 15-24. White women age 20 and older had higher visit rates to private medical services than to clinics. Never married women had higher visit rates to clinics than currently or formerly married women. Data were also collected in 1982 on use of medical services for infertility by women who had difficulty in conceiving or carrying a pregnancy to term. About 1 million ever married women had 1 or more infertility visits in the 12 months before the interview. During the 3 years before interview, about 1.9 million women had infertility visits. For all ever married women, as well as for white and black women separately, infertility services were more likely to be secured from private medical sources than from clinics. The survey design, reliability of the estimates and the terms used are explained in the technical notes.
