A new concept for tuning design weights in survey sampling : jackknifing in theory and practice / [electronic resource]
by Singh, Sarjinder [author.]; Sedory, Stephen [author.]; Rueda, Maria [author.]; Arcos, Antonio [author.]; Arnab, Raghunath [author.].
Material type:
BookPublisher: Amsterdam : Academic Press, 2015.Description: 1 online resource.ISBN: 9780081005958; 0081005954; 0081005946; 9780081005941.Subject(s): Sampling (Statistics) | Sampling (Statistics) | Sampling (Statistics) | Mathematics -- Probability & Statistics -- General | Electronic books | Electronic booksOnline resources: ScienceDirect CIP data; item not viewed.
Front Cover; A New Concept for Tuning Design Weights in Survey Sampling: Jackknifing in Theory and Practice; Copyright; Dedication; Contents; Preface; Further studies; Acknowledgments; Chapter 1: Problem of estimation; 1.1. Introduction; 1.2. Estimation problem and notation; 1.3. Modeling of jumbo pumpkins; 1.3.1. R code; 1.4. The concept of jackknifing; 1.5. Jackknifing the sample mean; 1.6. Doubly jackknifed sample mean; 1.7. Jackknifing a sample proportion; 1.8. Jackknifing of a double suffix variable sum; 1.9. Frequently asked questions; 1.10. Exercises.
Chapter 2: Tuning of jackknife estimator2.1. Introduction; 2.2. Notation; 2.3. Tuning with a chi-square type distance function; 2.3.1. Problem of undercoverage; 2.3.2. Estimation of variance and coverage; 2.3.3. R code; 2.3.4. Remark on tuning with a chi-square distance; 2.3.5. Numerical illustration; 2.3.6. R code used for illustration; 2.3.7. Problem of negative weights; 2.4. Tuning with dell function; 2.4.1. Estimation of variance and coverage; 2.4.2. R code; 2.4.3. Numerical illustration; 2.4.4. R code used for illustration; 2.5. An important remark; 2.6. Exercises.
Chapter 3: Model assisted tuning of estimators3.1. Introduction; 3.2. Model assisted tuning with a chi-square distance function; 3.2.1. Estimation of variance and coverage; 3.2.2. R code; 3.3. Model assisted tuning with a dual-to-empirical log-likelihood (dell) function; 3.3.1. Estimation of variance and coverage; 3.3.2. R code; 3.4. Exercises; Chapter 4: Tuned estimators of finite population variance; 4.1. Introduction; 4.2. Tuned estimator of finite population variance; 4.3. Tuning with a chi-square distance; 4.3.1. Estimation of variance of the estimator of variance and coverage.
4.3.2. R code4.3.3. Remark on tuning with a chi-square distance; 4.3.4. Numerical illustration; 4.3.5. R code used for illustration; 4.3.6. F-distribution; 4.4. Tuning of estimator of finite population variance with a dual-to-empirical log-likelihood (dell) function; 4.4.1. Estimation of variance and coverage; 4.4.2. R code; 4.4.3. Numerical illustration; 4.4.4. R code used for illustration; 4.5. Alternative tuning with a chi-square distance; 4.5.1. Estimation of variance and coverage; 4.5.2. R code; 4.5.3. Numerical illustration; 4.5.4. R code used for illustration.
4.6. Alternative tuning with a dell function4.6.1. Estimation of variance and coverage; 4.6.2. R code; 4.6.3. Numerical illustration; 4.6.4. R code used for illustration; 4.7. Exercises; Chapter 5: Tuned estimators of correlation coefficient; 5.1. Introduction; 5.2. Correlation coefficient; 5.3. Tuned estimator of correlation coefficient; 5.3.1. Estimation of variance of the estimator of correlation coefficient and coverage; 5.3.2. R code; 5.3.3. Numerical illustration; 5.3.4. R code used for illustration; 5.4. Exercises; Chapter 6: Tuning of multicharacter survey estimators.
A New Concept for Tuning Design Weights in Survey Sampling: Jackknifing in Theory and Practice introduces the new concept of tuning design weights in survey sampling by presenting three concepts: calibration, jackknifing, and imputing where needed. This new methodology allows survey statisticians to develop statistical software for analyzing data in a more precisely and friendly way than with existing techniques.
Includes bibliographical references and indexes.
There are no comments for this item.