LEADER 04782nam 2200565 450 001 9910797838503321 005 20200520144314.0 010 $a0-08-100595-4 035 $a(CKB)3710000000513629 035 $a(EBL)4097056 035 $a(Au-PeEL)EBL4097056 035 $a(CaPaEBR)ebr11121297 035 $a(CaONFJC)MIL871503 035 $a(OCoLC)932332309 035 $a(MiAaPQ)EBC4097056 035 $a(PPN)193663457 035 $a(EXLCZ)993710000000513629 100 $a20151208h20162016 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 12$aA new concept for tuning design weights in survey sampling $ejackknifing in theory and practice /$fby 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 210 1$aLondon :$cElsevier,$d[2016] 210 4$dİ2016 215 $a1 online resource (318 p.) 300 $aDescription based upon print version of record. 311 $a0-08-100594-6 320 $aIncludes bibliographical references and indexes. 327 $aFront 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 327 $aChapter 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 327 $aChapter 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 327 $a4.3.2. 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 327 $a4.6. 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 327 $a6.1. Introduction 606 $aSampling (Statistics) 615 0$aSampling (Statistics) 700 $aSingh$b Sarjinder$01559742 702 $aSedory$b Stephen 702 $aDel Mar Rueda$b Maria 702 $aArcos$b Antonio 702 $aArnab$b Raghunath 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910797838503321 996 $aA new concept for tuning design weights in survey sampling$93825160 997 $aUNINA