Download or read book Calculation of Nonparametric Confidence Intervals by the Bootstrap Method written by Michael R. Emptage and published by . This book was released on 1984 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Bootstrap written by Andrea D. Volpe and published by . This book was released on 2005 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bootstrapping written by Christopher Z. Mooney and published by SAGE. This book was released on 1993-08-09 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is. . . clear and well-written. . . anyone with any interest in the basis of quantitative analysis simply must read this book. . . . well-written, with a wealth of explanation. . ." --Dougal Hutchison in Educational Research Using real data examples, this volume shows how to apply bootstrapping when the underlying sampling distribution of a statistic cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, it discusses the advantages and limitations of four bootstrap confidence interval methods--normal approximation, percentile, bias-corrected percentile, and percentile-t. The book concludes with a convenient summary of how to apply this computer-intensive methodology using various available software packages.
Download or read book Statistical Inference via Data Science A ModernDive into R and the Tidyverse written by Chester Ismay and published by CRC Press. This book was released on 2019-12-23 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for "tidy" and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive.com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels.
Download or read book Bootstrap Methods and Their Application written by A. C. Davison and published by Cambridge University Press. This book was released on 1997-10-28 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: Disk contains the library functions and documentation for use with Splus for Windows.
Download or read book An Introduction to the Bootstrap written by Bradley Efron and published by CRC Press. This book was released on 1994-05-15 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
Download or read book Nonparametric Bootstrap Confidence Intervals for High Quantiles of Heavy Tailed Distributions written by Svetlana Litvinova and published by . This book was released on 2018 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: In risk management areas such as reinsurance, the need often arises to construct a confidence interval for a quantile in the tail of the distribution; for example, there is high probability that the sample maximum lies near or below the quantile. While different methods, including subsample bootstrap, are available for this purpose, a full-sample bootstrap has not been shown to be valid. In fact, doubts have been raised about the validity of full-sample bootstrap. In this paper, we show that the full-sample bootstrap is valid for constructing a confidence interval for a high-quantile of heavy-tailed distribution. This is a significant theoretical contribution to the topic, and opens the possibility of full-sample bootstrap for inference on tails in higher dimensions. In our simulation study, our bootstrap method performed overall better than the data tilting method, but none was uniformly the best; the data tilting method appears to be currently the preferred choice. Since the two methods are based on quite different approaches but performed competitively in our simulation, we recommend that both methods be used side by side in applications.
Download or read book The Jackknife the Bootstrap and Other Resampling Plans written by Bradley Efron and published by SIAM. This book was released on 1982-01-01 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: The jackknife and the bootstrap are nonparametric methods for assessing the errors in a statistical estimation problem. They provide several advantages over the traditional parametric approach: the methods are easy to describe and they apply to arbitrarily complicated situations; distribution assumptions, such as normality, are never made. This monograph connects the jackknife, the bootstrap, and many other related ideas such as cross-validation, random subsampling, and balanced repeated replications into a unified exposition. The theoretical development is at an easy mathematical level and is supplemented by a large number of numerical examples. The methods described in this monograph form a useful set of tools for the applied statistician. They are particularly useful in problem areas where complicated data structures are common, for example, in censoring, missing data, and highly multivariate situations.
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 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.
Download or read book Reliability of Confidence Intervals Calculated by Bootstrap and Classical Methods Using the FIA 1 Ha Plot Design written by Hans T. Schreuder and published by . This book was released on 2000 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Introduction to Robust Estimation and Hypothesis Testing written by Rand R. Wilcox and published by Academic Press. This book was released on 2012-01-12 with total page 713 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book focuses on the practical aspects of modern and robust statistical methods. The increased accuracy and power of modern methods, versus conventional approaches to the analysis of variance (ANOVA) and regression, is remarkable. Through a combination of theoretical developments, improved and more flexible statistical methods, and the power of the computer, it is now possible to address problems with standard methods that seemed insurmountable only a few years ago"--
Download or read book Statistics with Confidence written by Douglas Altman and published by John Wiley & Sons. This book was released on 2013-06-03 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: This highly popular introduction to confidence intervals has been thoroughly updated and expanded. It includes methods for using confidence intervals, with illustrative worked examples and extensive guidelines and checklists to help the novice.
Download or read book Topics in Nonparametric Regression written by Christian David Galindo and published by . This book was released on 1998 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Applied Nonparametric Statistical Methods written by Peter Sprent and published by CRC Press. This book was released on 2016-04-19 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: While preserving the clear, accessible style of previous editions, Applied Nonparametric Statistical Methods, Fourth Edition reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets. Reorganized and with additional material, this edition begins with a brief summary of some
Download or read book Covariate Adjusted Confidence Intervals for the Incremental Cost Effectiveness Ratio in Non Randomized Trails Comparing Fieller and Bootstrap Methods An Example from the Sarah Study written by Bernd Schweikert and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Background: Assessment of uncertainty in cost-effectiveness analysis is of high importance as it greatly impacts use of cost-effectiveness results in decision making and priority setting. While in recent years various methods have been suggested for the estimation of confidence intervals to the incremental cost-effectiveness ratio (ICER) when groups are randomly assigned to the treatment groups, relatively little work has been done in the case of non-randomized studies. Objective: Aim of this study is to describe a method of resampling based confidence intervals in the context of non-randomized trial requiring covariate adjustment and to compare it to a recently suggested parametric approach. Its suitability and first indication of relative performance are discussed using data from a non randomized trial comparing inpatient and outpatient rehabilitation in patients after an acute myocardial infarction (SARAH-Study). Methods: Covariate adjustment was performed using a seemingly unrelated regression (SURE) approach. Regression equations for costs and effects were jointly estimated allowing to incorporate the correlation between cost and effects. Point estimate of the ICER ware calculated by the quotient of the coefficient of the therapy-dummy in the cost and effect equation. Parametric confidence intervals for the ICER were calculated using the Fieller formula. Non parametric approach was based on bootstrapping the SURE regression model. Resampled point estimates of the ICER were transformed into angular deviations and standard methods for bootstrap confidence intervals were applied. Results: The 163 patients enrolled into the SARAH-trial preferred predominantly the inpatient to the outpatient rehabilitation (112 vs. 51). Based on pre-post rehabilitation data patients in the outpatient group incurred lower gains in quality of life (p=0,094) but caused also considerably lower cost (p0,001). Fieller confidence intervals for the ICER were restricted to significance levels of 0.12 since conclusions of the analytic term became complex with smaller levels of alpha, reflecting the insignificance of the effect estimate. Due to the non-parametric nature of the bootstrap method intervals could be calculated for any confidence level. They also tended to be wider for the same alpha levels compared to Fieller confidence intervals. Conclusion: Assessment of the uncertainty in the ICER is crucial also in a non randomized context. Different methods can be applied for this purpose. The Fieller method which is based on the normality assumptions tends to give exact solution if assumptions are met. In the example however it exhibited its known problem of yielding no solutions for alpha levels of interest if differences in effects and/or costs are too small. The approach of bootstrapping the regression seemed here suitable, although it tended to show wider confidence intervals. This non-parametric approach offered a valuable option in this non randomized trial setting, of which a detailed analysis of performance will be investigated in further simulation analyses.
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.
