Download or read book Improving Coverage Accuracy of Nonparametric Prediction Intervals written by Peter Hall and published by . This book was released on 2001 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Prediction Intervals written by Lan Chou and published by . This book was released on 1997 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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.
Download or read book Visualizing and Forecasting Functional Time Series written by Han Lin Shang and published by . This book was released on 2010 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the recent statistical literature, considerable attention has been paid to the development of functional data analysis. In particular, there have been many theoretical and practical developments in clustering and modeling of functional data. However, the development of visualizing and forecasting functional data is still very limited. The aim of this thesis is to develop new techniques for visualizing, modeling and forecasting functional data.The first contribution of this thesis is to propose three graphical tools for visualizing the pattern of functional data in the form of smooth curves or surfaces. The proposed tools include functional versions of the bagplot and the highest density region (HDR) boxplot, which make use of the first two robust principal component scores, Tukey's halfspace location depth and highest density regions. As a by-product, the functional bagplot and the functional HDR boxplot can also be used to detect functional outliers if they are present in the data. Their outlier detection performances are compared favorably with those of their competitors using two real data sets and a series of simulation studies.The second contribution is to propose two nonparametric methods, namely weighted functional principal component regression and weighted functional partial least squares regression, for forecasting functional time series. These approaches allow smooth functions, and assign more weight to more recent data than to data from the distant past. They also provide a modeling scheme that can easily be adapted to take constraints and other information into account. Using the data sets of French female mortality rates and Australian fertility rates, I demonstrate that these two weighted methods perform similarly, but that they both have improved point forecast accuracy relative to those of their unweighted counterparts. Furthermore, I propose two new bootstrap methods for constructing prediction intervals, and evaluate and compare their empirical coverage probability.The third contribution is to further examine the point forecast accuracy and interval forecastaccuracy of the weighted functional principal component regression for forecasting log mortality rates and life expectancy. Using the age- and sex-specific populations of 14 developed countries, I compare the short- to medium-term accuracy of this newly proposed method with those of nine well-established methods in the fields of demography and statistics. The weighted functional principal component regression achieves the best point forecast accuracy and interval forecast accuracy for log mortality rates. However, this does not necessarily translate into the best forecast accuracy for life expectancy. Therefore, I also examine which approach achieves the best point forecast accuracy and interval forecast accuracy for life expectancy.Finally, I develop a nonparametric method for forecasting seasonal univariate time series. A univariate time series with N = np data points is divided into n functional time series with the function support range [x1; xp]. The forecasting method reduces the data dimensionality by functional principal component analysis, and then applies univariate time series forecasting and functional principal component regression techniques. When partial data in the most recent curve are observed, four dynamic updating methods are introduced, namely the block moving method, the ordinary least squares method, the ridge regression method, and the penalized least squares method. Using a data set of monthly sea surface temperatures between 1950 and 2008, I compare the dynamic updating methods with several benchmark methods, and show their superior point forecast accuracy and interval forecast accuracy. Furthermore, a nonparametric approach is introduced to construct prediction intervals for an entire forecast curve or part thereof.
Download or read book A Nonparametric approach to the construction of prediction intervals for time series forecasts Working Paper No 63 written by W.Allen Spivey and William W. Wecker and published by . This book was released on 1972 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistica Sinica written by and published by . This book was released on 1998 with total page 1350 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Prediction Interval Estimation Techniques for Empirical Modeling Strategies and Their Applications to Signal Validation Tasks written by and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The basis of this work was to evaluate both parametric and non-parametric empirical modeling strategies applied to signal validation or on-line monitoring tasks. On-line monitoring methods assess signal channel performance to aid in making instrument calibration decisions, enabling the use of condition-based calibration schedules. The three non-linear empirical modeling strategies studied were: artificial neural networks(ANN), neural network partial least squares (NNPLS), and local polynomial regression (LPR). These three types are the most common nonlinear models for applications to signal validation tasks. Of the class of local polynomials (for LPR), two were studied in this work: zero-order (kernel regression), and first-order (local linear regression). The evaluation of the empirical modeling strategies includes the presentation and derivation of prediction intervals for each of three different model types studied so that estimations could be made with an associated prediction interval. An estimate and its corresponding prediction interval contain the measurements with a specified certainty, usually 95%. The prediction interval estimates were compared to results obtained from bootstrapping via Monte Carlo resampling, to validate their expected accuracy. The estimation of prediction intervals applied to on-line monitoring systems is essential if widespread use of these empirical based systems is to be attained. In response to the topical report "On-Line Monitoring of Instrument Channel Performance," published by the Electric Power Research Institute [Davis 1998], the NRC issued a safety evaluation report that identified the need to evaluate the associated uncertainty of empirical model estimations from all contributing sources. This need forms the basis for the research completed and reported in this dissertation. The focus of this work, and basis of its original contributions, were to provide an accurate prediction interval estimation method for each of the mentioned empirical modeling techniques, and to verify the results via bootstrap simulation studies. Properly determined prediction interval estimates were obtained that consistently captured the uncertainty of the given model such that the level of certainty of the intervals closely matched the observed level of coverage of the prediction intervals over the measured values. a given set of specifications, the identification of this optimal set may be difficult to attain.
Download or read book Expected Coverage of Prediction Intervals in a Two parameter Non linear Model written by A. H. Pooi and published by . This book was released on 1986 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Statistics written by Michele La Rocca and published by Springer Nature. This book was released on 2020-11-11 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: Highlighting the latest advances in nonparametric and semiparametric statistics, this book gathers selected peer-reviewed contributions presented at the 4th Conference of the International Society for Nonparametric Statistics (ISNPS), held in Salerno, Italy, on June 11-15, 2018. It covers theory, methodology, applications and computational aspects, addressing topics such as nonparametric curve estimation, regression smoothing, models for time series and more generally dependent data, varying coefficient models, symmetry testing, robust estimation, and rank-based methods for factorial design. It also discusses nonparametric and permutation solutions for several different types of data, including ordinal data, spatial data, survival data and the joint modeling of both longitudinal and time-to-event data, permutation and resampling techniques, and practical applications of nonparametric statistics. The International Society for Nonparametric Statistics is a unique global organization, and its international conferences are intended to foster the exchange of ideas and the latest advances and trends among researchers from around the world and to develop and disseminate nonparametric statistics knowledge. The ISNPS 2018 conference in Salerno was organized with the support of the American Statistical Association, the Institute of Mathematical Statistics, the Bernoulli Society for Mathematical Statistics and Probability, the Journal of Nonparametric Statistics and the University of Salerno.
Download or read book Higher Order Asymptotics written by J. K. Ghosh and published by IMS. This book was released on 1994 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Quality Control and Applied Statistics written by and published by . This book was released on 2002 with total page 796 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Models and Methods for Lifetime Data written by Jerald F. Lawless and published by John Wiley & Sons. This book was released on 2011-01-25 with total page 662 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the First Edition "An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . ." -Choice "This is an important book, which will appeal to statisticians working on survival analysis problems." -Biometrics "A thorough, unified treatment of statistical models and methods used in the analysis of lifetime data . . . this is a highly competent and agreeable statistical textbook." -Statistics in Medicine The statistical analysis of lifetime or response time data is a key tool in engineering, medicine, and many other scientific and technological areas. This book provides a unified treatment of the models and statistical methods used to analyze lifetime data. Equally useful as a reference for individuals interested in the analysis of lifetime data and as a text for advanced students, Statistical Models and Methods for Lifetime Data, Second Edition provides broad coverage of the area without concentrating on any single field of application. Extensive illustrations and examples drawn from engineering and the biomedical sciences provide readers with a clear understanding of key concepts. New and expanded coverage in this edition includes: * Observation schemes for lifetime data * Multiple failure modes * Counting process-martingale tools * Both special lifetime data and general optimization software * Mixture models * Treatment of interval-censored and truncated data * Multivariate lifetimes and event history models * Resampling and simulation methodology
Download or read book Algorithmic Learning in a Random World written by Vladimir Vovk and published by Springer Science & Business Media. This book was released on 2005-03-22 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.
Download or read book Generalized Linear Mixed Models written by Charles E. McCulloch and published by IMS. This book was released on 2003 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wiley Series in Probability and Statistics A modern perspective on mixed models The availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data. As a follow-up to Searle's classic, Linear Models, and Variance Components by Searle, Casella, and McCulloch, this new work progresses from the basic one-way classification to generalized linear mixed models. A variety of statistical methods are explained and illustrated, with an emphasis on maximum likelihood and restricted maximum likelihood. An invaluable resource for applied statisticians and industrial practitioners, as well as students interested in the latest results, Generalized, Linear, and Mixed Models features: * A review of the basics of linear models and linear mixed models * Descriptions of models for nonnormal data, including generalized linear and nonlinear models * Analysis and illustration of techniques for a variety of real data sets * Information on the accommodation of longitudinal data using these models * Coverage of the prediction of realized values of random effects * A discussion of the impact of computing issues on mixed models
Download or read book Statistical Intervals written by William Q. Meeker and published by John Wiley & Sons. This book was released on 2017-03-09 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: Describes statistical intervals to quantify sampling uncertainty,focusing on key application needs and recently developed methodology in an easy-to-apply format Statistical intervals provide invaluable tools for quantifying sampling uncertainty. The widely hailed first edition, published in 1991, described the use and construction of the most important statistical intervals. Particular emphasis was given to intervals—such as prediction intervals, tolerance intervals and confidence intervals on distribution quantiles—frequently needed in practice, but often neglected in introductory courses. Vastly improved computer capabilities over the past 25 years have resulted in an explosion of the tools readily available to analysts. This second edition—more than double the size of the first—adds these new methods in an easy-to-apply format. In addition to extensive updating of the original chapters, the second edition includes new chapters on: Likelihood-based statistical intervals Nonparametric bootstrap intervals Parametric bootstrap and other simulation-based intervals An introduction to Bayesian intervals Bayesian intervals for the popular binomial, Poisson and normal distributions Statistical intervals for Bayesian hierarchical models Advanced case studies, further illustrating the use of the newly described methods New technical appendices provide justification of the methods and pathways to extensions and further applications. A webpage directs readers to current readily accessible computer software and other useful information. Statistical Intervals: A Guide for Practitioners and Researchers, Second Edition is an up-to-date working guide and reference for all who analyze data, allowing them to quantify the uncertainty in their results using statistical intervals.
Download or read book Statistical Methods in Water Resources written by D.R. Helsel and published by Elsevier. This book was released on 1993-03-03 with total page 539 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data on water quality and other environmental issues are being collected at an ever-increasing rate. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources. The last fifteen years have seen major advances in the fields of exploratory data analysis (EDA) and robust statistical methods. The 'real-life' characteristics of environmental data tend to drive analysis towards the use of these methods. These advances are presented in a practical and relevant format. Alternate methods are compared, highlighting the strengths and weaknesses of each as applied to environmental data. Techniques for trend analysis and dealing with water below the detection limit are topics covered, which are of great interest to consultants in water-quality and hydrology, scientists in state, provincial and federal water resources, and geological survey agencies. The practising water resources scientist will find the worked examples using actual field data from case studies of environmental problems, of real value. Exercises at the end of each chapter enable the mechanics of the methodological process to be fully understood, with data sets included on diskette for easy use. The result is a book that is both up-to-date and immediately relevant to ongoing work in the environmental and water sciences.
Download or read book Statistical Methods for Reliability Data written by William Q. Meeker and published by John Wiley & Sons. This book was released on 2022-01-24 with total page 708 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative guide to the most recent advances in statistical methods for quantifying reliability Statistical Methods for Reliability Data, Second Edition (SMRD2) is an essential guide to the most widely used and recently developed statistical methods for reliability data analysis and reliability test planning. Written by three experts in the area, SMRD2 updates and extends the long- established statistical techniques and shows how to apply powerful graphical, numerical, and simulation-based methods to a range of applications in reliability. SMRD2 is a comprehensive resource that describes maximum likelihood and Bayesian methods for solving practical problems that arise in product reliability and similar areas of application. SMRD2 illustrates methods with numerous applications and all the data sets are available on the book’s website. Also, SMRD2 contains an extensive collection of exercises that will enhance its use as a course textbook. The SMRD2's website contains valuable resources, including R packages, Stan model codes, presentation slides, technical notes, information about commercial software for reliability data analysis, and csv files for the 93 data sets used in the book's examples and exercises. The importance of statistical methods in the area of engineering reliability continues to grow and SMRD2 offers an updated guide for, exploring, modeling, and drawing conclusions from reliability data. SMRD2 features: Contains a wealth of information on modern methods and techniques for reliability data analysis Offers discussions on the practical problem-solving power of various Bayesian inference methods Provides examples of Bayesian data analysis performed using the R interface to the Stan system based on Stan models that are available on the book's website Includes helpful technical-problem and data-analysis exercise sets at the end of every chapter Presents illustrative computer graphics that highlight data, results of analyses, and technical concepts Written for engineers and statisticians in industry and academia, Statistical Methods for Reliability Data, Second Edition offers an authoritative guide to this important topic.
