A new concept for tuning design weights in survey sampling : jackknifing in theory and practice / by Sarjinder Singh, Texas A&M University-Kingsville, TX, USA ; Stephen A. Sedory, Texas A&M University-Kingsville, TX, USA, Maria del Mar Rueda, University of Granada, Spain, Antonio Arcos, University of Granada, Spain, Raghunath Arnab, University of Botswana and University of KwaZulu-Natal, S. Africa.

Format:

Book

Author/Creator:

Singh, Sarjinder, author.

Sedory, Stephen, author.

Del Mar Rueda, Maria, author.

Arcos Cebrián, Antonio, author.

Arnab, Raghunath, author.

Language:

English

Subjects (All):

Sampling (Statistics).

Physical Description:

1 online resource (318 p.)

Edition:

1st ed.

Place of Publication:

London : Elsevier, [2016]

Summary:

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.

Contents:

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

6.1. Introduction

Notes:

Description based upon print version of record.

Includes bibliographical references and indexes.

Description based on online resource; title from PDF title page (ebrary, viewed December 8, 2015).

Description based on publisher supplied metadata and other sources.

ISBN:

9780081005958

0081005954

OCLC:

932332309