Empirical Likelihood Methods In Biomedicine And Health PDF Download
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Author | : Albert Vexler |
Publisher | : CRC Press |
Total Pages | : 300 |
Release | : 2018-09-03 |
Genre | : Mathematics |
ISBN | : 1351001515 |
Download Empirical Likelihood Methods in Biomedicine and Health Book in PDF, ePub and Kindle
Empirical Likelihood Methods in Biomedicine and Health provides a compendium of nonparametric likelihood statistical techniques in the perspective of health research applications. It includes detailed descriptions of the theoretical underpinnings of recently developed empirical likelihood-based methods. The emphasis throughout is on the application of the methods to the health sciences, with worked examples using real data. Provides a systematic overview of novel empirical likelihood techniques. Presents a good balance of theory, methods, and applications. Features detailed worked examples to illustrate the application of the methods. Includes R code for implementation. The book material is attractive and easily understandable to scientists who are new to the research area and may attract statisticians interested in learning more about advanced nonparametric topics including various modern empirical likelihood methods. The book can be used by graduate students majoring in biostatistics, or in a related field, particularly for those who are interested in nonparametric methods with direct applications in Biomedicine.
Author | : Art B. Owen |
Publisher | : CRC Press |
Total Pages | : 322 |
Release | : 2001-05-18 |
Genre | : Mathematics |
ISBN | : 1420036157 |
Download Empirical Likelihood Book in PDF, ePub and Kindle
Empirical likelihood provides inferences whose validity does not depend on specifying a parametric model for the data. Because it uses a likelihood, the method has certain inherent advantages over resampling methods: it uses the data to determine the shape of the confidence regions, and it makes it easy to combined data from multiple sources. It al
Author | : Yanmei Xie |
Publisher | : |
Total Pages | : 125 |
Release | : 2019 |
Genre | : Estimation theory |
ISBN | : |
Download Empirical Likelihood Methods in Nonignorable Covariate-missing Data Problems Book in PDF, ePub and Kindle
Missing covariate data occurs often in regression analysis, which frequently arises in the health and social sciences as well as in survey sampling. This dissertation contains three topics in nonignorable covariate-missing data problems, in which we study methods for the analysis of a nonignorable covariate-missing data problem in an assumed conditional mean function when some covariates are completely observed but other covariates are missing for some subjects. First, by exploitation of a probability model of missingness and a working conditional score model from a semiparametric perspective, we propose a unified approach to constructing a system of unbiased estimating equations, where there are more equations than unknown parameters of interest. These unbiased estimating equations naturally incorporate the incomplete data into the data analysis, making it possible to seek efficient estimation of the parameter of interest even when the working regression function is not specified to be the optimal regression function. Based on the proposed estimating equations, we introduce three maximum empirical likelihood estimators of the underlying regression parameters and compare their efficiencies with other existing competitors. By utilizing the proposed empirical likelihood method on a data set from the US National Health and Nutrition Examination Survey (NHANES), we study the effect of daily alcohol consumption on hypertension. Second, we explore unconstrained and constrained empirical likelihood ratio statistics to construct empirical likelihood confidence regions for the underlying regression parameters without and with constraints. We establish the asymptotic distributions of the proposed empirical likelihood ratio statistics. The proposed empirical likelihood methods have a better finite-sample performance than other existing competitors in terms of coverage probability and interval length. An analysis on the data set from the US NHANES demonstrates that increased alcohol consumption per day is significantly associated with increased systolic blood pressure. In addition, higher body mass index and older age have a significantly higher risk of hypertension. Third, we propose a pseudo empirical likelihood ratio statistic, yet it is demonstrated following an asymptotically chi-squared distribution. Our proposed method allows for confidence interval construction without variance estimation and thus is more computationally feasible. Simulation results suggest that the proposed empirical likelihood confidence interval has a better finite-sample performance than the corresponding Wald-based competitor in terms of coverage probability and interval length. Moreover, the proposed empirical likelihood ratio test is always superior to the Wald method in terms of their power performances in our simulation studies.
Author | : Fan Wu |
Publisher | : |
Total Pages | : |
Release | : 2015 |
Genre | : |
ISBN | : |
Download The Extended Empirical Likelihood Book in PDF, ePub and Kindle
The empirical likelihood method introduced by Owen (1988, 1990) is a powerful nonparametric method for statistical inference. It has been one of the most researched methods in statistics in the last twenty-five years and remains to be a very active area of research today. There is now a large body of literature on empirical likelihood method which covers its applications in many areas of statistics (Owen, 2001). One important problem affecting the empirical likelihood method is its poor accuracy, especially for small sample and/or high-dimension applications. The poor accuracy can be alleviated by using high-order empirical likelihood methods such as the Bartlett corrected empirical likelihood but it cannot be completely resolved by high-order asymptotic methods alone. Since the work of Tsao (2004), the impact of the convex hull constraint in the formulation of the empirical likelihood on the finite sample accuracy has been better understood, and methods have been developed to break this constraint in order to improve the accuracy.
Author | : Mai Zhou |
Publisher | : CRC Press |
Total Pages | : 221 |
Release | : 2015-06-17 |
Genre | : Mathematics |
ISBN | : 1466554932 |
Download Empirical Likelihood Method in Survival Analysis Book in PDF, ePub and Kindle
Empirical Likelihood Method in Survival Analysis explains how to use the empirical likelihood method for right censored survival data. The author uses R for calculating empirical likelihood and includes many worked out examples with the associated R code. The datasets and code are available for download on his website and CRAN. The book focuses on all the standard survival analysis topics treated with empirical likelihood, including hazard functions, cumulative distribution functions, analysis of the Cox model, and computation of empirical likelihood for censored data. It also covers semi-parametric accelerated failure time models, the optimality of confidence regions derived from empirical likelihood or plug-in empirical likelihood ratio tests, and several empirical likelihood confidence band results. While survival analysis is a classic area of statistical study, the empirical likelihood methodology has only recently been developed. Until now, just one book was available on empirical likelihood and most statistical software did not include empirical likelihood procedures. Addressing this shortfall, this book provides the functions to calculate the empirical likelihood ratio in survival analysis as well as functions related to the empirical likelihood analysis of the Cox regression model and other hazard regression models.
Author | : Yichuan Zhao |
Publisher | : |
Total Pages | : 362 |
Release | : 2002 |
Genre | : Analytic functions |
ISBN | : |
Download Empirical Likelihood Methods for Comparison of Survival Functions Book in PDF, ePub and Kindle
Author | : Zhong Guan |
Publisher | : |
Total Pages | : 358 |
Release | : 2001 |
Genre | : Statistical hypothesis testing |
ISBN | : |
Download Some Results about Empirical Likelihood Method Book in PDF, ePub and Kindle
Author | : N. Balakrishnan |
Publisher | : Wiley |
Total Pages | : 960 |
Release | : 2014-05-16 |
Genre | : Medical |
ISBN | : 9781118595886 |
Download Methods and Applications of Statistics in Clinical Trials, Volume 2 Book in PDF, ePub and Kindle
"This comprehensive book features both new and established material on the key statistical principles and concepts for designing modern-day clinical trials, such as hazard ratio, flexible designs, confounding, covariates, missing data, and longitudinal data. It discusses the various kinds of trials that can be found in today's clinical setting including open-labeled trials, multicentered trials, and superiority trials. It also explores such ongoing, cutting-edge trials as early cancer & heart disease, mother to child human immunodeficiency virus transmission, women's health initiative dietary, and AIDS"--Provided by publisher.
Author | : Alex Bottle |
Publisher | : CRC Press |
Total Pages | : 292 |
Release | : 2016-08-05 |
Genre | : Mathematics |
ISBN | : 1482246104 |
Download Statistical Methods for Healthcare Performance Monitoring Book in PDF, ePub and Kindle
Healthcare is important to everyone, yet large variations in its quality have been well documented both between and within many countries. With demand and expenditure rising, it’s more crucial than ever to know how well the healthcare system and all its components – from staff member to regional network – are performing. This requires data, which inevitably differ in form and quality. It also requires statistical methods, the output of which needs to be presented so that it can be understood by whoever needs it to make decisions. Statistical Methods for Healthcare Performance Monitoring covers measuring quality, types of data, risk adjustment, defining good and bad performance, statistical monitoring, presenting the results to different audiences and evaluating the monitoring system itself. Using examples from around the world, it brings all the issues and perspectives together in a largely non-technical way for clinicians, managers and methodologists. Statistical Methods for Healthcare Performance Monitoring is aimed at statisticians and researchers who need to know how to measure and compare performance, health service regulators, health service managers with responsibilities for monitoring performance, and quality improvement scientists, including those involved in clinical audits.
Author | : Albert Vexler |
Publisher | : CRC Press |
Total Pages | : 703 |
Release | : 2017-12-19 |
Genre | : Mathematics |
ISBN | : 1498730841 |
Download Statistical Testing Strategies in the Health Sciences Book in PDF, ePub and Kindle
Statistical Testing Strategies in the Health Sciences provides a compendium of statistical approaches for decision making, ranging from graphical methods and classical procedures through computationally intensive bootstrap strategies to advanced empirical likelihood techniques. It bridges the gap between theoretical statistical methods and practical procedures applied to the planning and analysis of health-related experiments. The book is organized primarily based on the type of questions to be answered by inference procedures or according to the general type of mathematical derivation. It establishes the theoretical framework for each method, with a substantial amount of chapter notes included for additional reference. It then focuses on the practical application for each concept, providing real-world examples that can be easily implemented using corresponding statistical software code in R and SAS. The book also explains the basic elements and methods for constructing correct and powerful statistical decision-making processes to be adapted for complex statistical applications. With techniques spanning robust statistical methods to more computationally intensive approaches, this book shows how to apply correct and efficient testing mechanisms to various problems encountered in medical and epidemiological studies, including clinical trials. Theoretical statisticians, medical researchers, and other practitioners in epidemiology and clinical research will appreciate the book’s novel theoretical and applied results. The book is also suitable for graduate students in biostatistics, epidemiology, health-related sciences, and areas pertaining to formal decision-making mechanisms.