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Bootstrap And Box Jenkins Prediction Intervals For Non Gaussian Autoregressive Models
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Book Synopsis Bootstrap and Box-Jenkins Prediction Intervals for Non-gaussian Autoregressive Models by : Elizabeth Kathleen Ely
Download or read book Bootstrap and Box-Jenkins Prediction Intervals for Non-gaussian Autoregressive Models written by Elizabeth Kathleen Ely and published by . This book was released on 1989 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Modified Approach for Obtaining Sieve Bootstrap Prediction Intervals for Time Series by : Purna Mukhopadhyay
Download or read book A Modified Approach for Obtaining Sieve Bootstrap Prediction Intervals for Time Series written by Purna Mukhopadhyay and published by . This book was released on 2008 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The traditional Box-Jenkins approach to obtaining prediction intervals for stationary time seres assumes that the underlying distribution of the innovations is Gaussian. It is well known that deviations from this assumption can lead to prediction intervals with poor coverage. Nonparametric bootstrap-based procedures for obtaining prediction intervals overcome this handicap, but many early versions of such intervals for autoregressive moving average (ARMA) processes assume that the autoregressive and moving average orders, p, q respectively, are known, The sieve bootstrap, first introduced by Bühlmann in 1997, sidesteps this assumption for invertible time series by approximating the ARMA process by a finite autoregressive model whose order is estimated by using a model procedure such as the AICC. Existing sieve bootstrap methods in general, however, produces liberal prediction intervals due to several factors, including the use of residuals that underestimate the actual variance of the innovations and the failure of the methods to capture variations due to sampling error of some parameter estimates. In this dissertation, a modified sieve bootstrap approach, that corrects these deficiencies, is implemented to obtain prediction intervals for both univariate and multivariate time series. Monte Carlo simulations results show that the modifications provide prediction intervals that achieve nominal or near nominal coverage probabilities. Asymptotic results for the univariate series also establish the validity of the modified approach"--Abstract, leaf iii.
Book Synopsis Bootstrap Methods by : Michael R. Chernick
Download or read book Bootstrap Methods written by Michael R. Chernick and published by John Wiley & Sons. This book was released on 2011-09-23 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical and accessible introduction to the bootstrap method——newly revised and updated Over the past decade, the application of bootstrap methods to new areas of study has expanded, resulting in theoretical and applied advances across various fields. Bootstrap Methods, Second Edition is a highly approachable guide to the multidisciplinary, real-world uses of bootstrapping and is ideal for readers who have a professional interest in its methods, but are without an advanced background in mathematics. Updated to reflect current techniques and the most up-to-date work on the topic, the Second Edition features: The addition of a second, extended bibliography devoted solely to publications from 1999–2007, which is a valuable collection of references on the latest research in the field A discussion of the new areas of applicability for bootstrap methods, including use in the pharmaceutical industry for estimating individual and population bioequivalence in clinical trials A revised chapter on when and why bootstrap fails and remedies for overcoming these drawbacks Added coverage on regression, censored data applications, P-value adjustment, ratio estimators, and missing data New examples and illustrations as well as extensive historical notes at the end of each chapter With a strong focus on application, detailed explanations of methodology, and complete coverage of modern developments in the field, Bootstrap Methods, Second Edition is an indispensable reference for applied statisticians, engineers, scientists, clinicians, and other practitioners who regularly use statistical methods in research. It is also suitable as a supplementary text for courses in statistics and resampling methods at the upper-undergraduate and graduate levels.
Book Synopsis Journal of the American Statistical Association by :
Download or read book Journal of the American Statistical Association written by and published by . This book was released on 1990 with total page 1248 pages. Available in PDF, EPUB and Kindle. Book excerpt: A scientific and educational journal not only for professional statisticians but also for economists, business executives, research directors, government officials, university professors, and others who are seriously interested in the application of statistical methods to practical problems, in the development of more useful methods, and in the improvement of basic statistical data.
Book Synopsis Bootstrap Prediction Intervals in State-Space Models by : Esther Ruiz
Download or read book Bootstrap Prediction Intervals in State-Space Models written by Esther Ruiz and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prediction intervals in state-space models can be obtained by assuming Gaussian innovations and using the prediction equations of the Kalman filter, with the true parameters substituted by consistent estimates. This approach has two limitations. First, it does not incorporate the uncertainty caused by parameter estimation. Second, the Gaussianity of future innovations assumption may be inaccurate. To overcome these drawbacks, Wall and Stoffer [Journal of Time Series Analysis (2002) Vol. 23, pp. 733-751] propose a bootstrap procedure for evaluating conditional forecast errors that requires the backward representation of the model. Obtaining this representation increases the complexity of the procedure and limits its implementation to models for which it exists. In this article, we propose a bootstrap procedure for constructing prediction intervals directly for the observations, which does not need the backward representation of the model. Consequently, its application is much simpler, without losing the good behaviour of bootstrap prediction intervals. We study its finite-sample properties and compare them with those of the standard and the Wall and Stoffer procedures for the local level model. Finally, we illustrate the results by implementing the new procedure to obtain prediction intervals for future values of a real time series.
Book Synopsis Time-Series Forecasting by : Chris Chatfield
Download or read book Time-Series Forecasting written by Chris Chatfield and published by CRC Press. This book was released on 2000-10-25 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the author of the bestselling "Analysis of Time Series," Time-Series Forecasting offers a comprehensive, up-to-date review of forecasting methods. It provides a summary of time-series modelling procedures, followed by a brief catalogue of many different time-series forecasting methods, ranging from ad-hoc methods through ARIMA and state-space
Book Synopsis Model-Free Prediction and Regression by : Dimitris N. Politis
Download or read book Model-Free Prediction and Regression written by Dimitris N. Politis and published by Springer. This book was released on 2015-11-13 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as opposed to unobservable model parameters and estimates thereof, and yields optimal predictors in diverse settings such as regression and time series. Furthermore, the Model-Free Bootstrap takes us beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality. Prediction has been traditionally approached via a model-based paradigm, i.e., (a) fit a model to the data at hand, and (b) use the fitted model to extrapolate/predict future data. Due to both mathematical and computational constraints, 20th century statistical practice focused mostly on parametric models. Fortunately, with the advent of widely accessible powerful computing in the late 1970s, computer-intensive methods such as the bootstrap and cross-validation freed practitioners from the limitations of parametric models, and paved the way towards the `big data' era of the 21st century. Nonetheless, there is a further step one may take, i.e., going beyond even nonparametric models; this is where the Model-Free Prediction Principle is useful. Interestingly, being able to predict a response variable Y associated with a regressor variable X taking on any possible value seems to inadvertently also achieve the main goal of modeling, i.e., trying to describe how Y depends on X. Hence, as prediction can be treated as a by-product of model-fitting, key estimation problems can be addressed as a by-product of being able to perform prediction. In other words, a practitioner can use Model-Free Prediction ideas in order to additionally obtain point estimates and confidence intervals for relevant parameters leading to an alternative, transformation-based approach to statistical inference.
Book Synopsis An Introduction to Bootstrap Methods with Applications to R by : Michael R. Chernick
Download or read book An Introduction to Bootstrap Methods with Applications to R written by Michael R. Chernick and published by John Wiley & Sons. This book was released on 2014-08-21 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to bootstrap methods in the R programming environment Bootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. An Introduction to Bootstrap Methods with Applications to R explores the practicality of this approach and successfully utilizes R to illustrate applications for the bootstrap and other resampling methods. This book provides a modern introduction to bootstrap methods for readers who do not have an extensive background in advanced mathematics. Emphasis throughout is on the use of bootstrap methods as an exploratory tool, including its value in variable selection and other modeling environments. The authors begin with a description of bootstrap methods and its relationship to other resampling methods, along with an overview of the wide variety of applications of the approach. Subsequent chapters offer coverage of improved confidence set estimation, estimation of error rates in discriminant analysis, and applications to a wide variety of hypothesis testing and estimation problems, including pharmaceutical, genomics, and economics. To inform readers on the limitations of the method, the book also exhibits counterexamples to the consistency of bootstrap methods. An introduction to R programming provides the needed preparation to work with the numerous exercises and applications presented throughout the book. A related website houses the book's R subroutines, and an extensive listing of references provides resources for further study. Discussing the topic at a remarkably practical and accessible level, An Introduction to Bootstrap Methods with Applications to R is an excellent book for introductory courses on bootstrap and resampling methods at the upper-undergraduate and graduate levels. It also serves as an insightful reference for practitioners working with data in engineering, medicine, and the social sciences who would like to acquire a basic understanding of bootstrap methods.
Book Synopsis Applied Computational Intelligence and Mathematical Methods by : Radek Silhavy
Download or read book Applied Computational Intelligence and Mathematical Methods written by Radek Silhavy and published by Springer. This book was released on 2017-09-04 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book discusses real-world problems and exploratory research in computational intelligence and mathematical models. It brings new approaches and methods to real-world problems and exploratory research that describes novel approaches in the mathematical methods, computational intelligence methods and software engineering in the scope of the intelligent systems. This book constitutes the refereed proceedings of the Computational Methods in Systems and Software 2017, a conference that provided an international forum for the discussion of the latest high-quality research results in all areas related to computational methods, statistics, cybernetics and software engineering.
Book Synopsis Bootstrap Prediction Intervals for Autoregressive Processes by : Lori Ann Thombs
Download or read book Bootstrap Prediction Intervals for Autoregressive Processes written by Lori Ann Thombs and published by . This book was released on 1986 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bootstrap Prediction Intervals for Multivariate Time Series by : Florian Sebastian Rueck
Download or read book Bootstrap Prediction Intervals for Multivariate Time Series written by Florian Sebastian Rueck and published by . This book was released on 2005 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The theory and methodology of obtaining bootstrap prediction intervals for univariate time series using the forward representation of the series is extended to vector autoregressive (VAR) models. Kim has shown that simultaneous prediction intervals based on the Bonferroni method and the backward representation of the time series achieve coverage close to nominal when the parameter estimates are corrected for small sample bias. To utilize his method, it is necessary to assume that the innovations are normally distributed to maintain independence of the innovations associated with the backward representation of the time series. This assumption is not necessary if the forward representation is used. Bootstrap prediction intervals based on the forward representation of the time series, are less restrictive and thus can also be adapted for time series that do not have a backward representation. The asymptotic validity of the proposed bootstrap method is established and small sample properties are studied using Monte Carlo simulation"--Abstract, leaf iii.
Book Synopsis Bootstrapping and Related Techniques by : Karl-Heinz Jöckel
Download or read book Bootstrapping and Related Techniques written by Karl-Heinz Jöckel and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains 30 selected, refereed papers from an in- ternational conference on bootstrapping and related techni- ques held in Trier 1990. Thepurpose of the book is to in- form about recent research in the area of bootstrap, jack- knife and Monte Carlo Tests. Addressing the novice and the expert it covers as well theoretical as practical aspects of these statistical techniques. Potential users in different disciplines as biometry, epidemiology, computer science, economics and sociology but also theoretical researchers s- hould consult the book to be informed on the state of the art in this area.
Book Synopsis Improved bootstrap forecast intervals for autoregressions by :
Download or read book Improved bootstrap forecast intervals for autoregressions written by and published by . This book was released on 1991 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: "We consider bootstrap construction and calibration of forecast intervals for non-Gaussian autoregressions. In particular, we address the question of prediction conditioned on the last 'p' observations of the process, for which we offer an exact simulation technique and an approximate bootstrap approach ..." -- on t.p.
Book Synopsis Probability and Mathematical Statistics by :
Download or read book Probability and Mathematical Statistics written by and published by . This book was released on 2005 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 1986 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Refinement of the Bootstrap Prediction Interval for Autoregressive Processes by : Mary Janette Schumann
Download or read book A Refinement of the Bootstrap Prediction Interval for Autoregressive Processes written by Mary Janette Schumann and published by . This book was released on 1986 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mathematical Reviews written by and published by . This book was released on 1998 with total page 716 pages. Available in PDF, EPUB and Kindle. Book excerpt:
