LEADER 05496nam 22006974a 450 001 9910143689003321 005 20170815121600.0 010 $a1-280-27623-1 010 $a9786610276233 010 $a0-470-01135-1 010 $a0-470-01134-3 035 $a(CKB)1000000000357448 035 $a(EBL)239034 035 $a(OCoLC)475950123 035 $a(SSID)ssj0000149684 035 $a(PQKBManifestationID)11147462 035 $a(PQKBTitleCode)TC0000149684 035 $a(PQKBWorkID)10239122 035 $a(PQKB)10338256 035 $a(MiAaPQ)EBC239034 035 $a(EXLCZ)991000000000357448 100 $a20050114d2005 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aEstimation in surveys with nonresponse$b[electronic resource] /$fCarl-Erik Sa?rndal, Sixten Lundstro?m 210 $aHoboken, NJ $cWiley$dc2005 215 $a1 online resource (214 p.) 225 1 $aWiley Series in Survey Methodology 300 $aDescription based upon print version of record. 311 $a0-470-01133-5 320 $aIncludes bibliographical references (p. [191]-194) and index. 327 $aEstimation in Surveys with Nonresponse; Contents; Preface; Chapter 1 Introduction; Chapter 2 The Survey and Its Imperfections; 2.1 The survey objective; 2.2 Sources of error in a survey; Chapter 3 General Principles to Assist Estimation; 3.1 Introduction; 3.2 The importance of auxiliary information; 3.3 Desirable features of an auxiliary vector; Chapter 4 The Use of Auxiliary Information under Ideal Conditions; 4.1 Introduction; 4.2 The Horvitz-Thompson estimator; 4.3 The generalized regression estimator; 4.4 Variance and variance estimation 327 $a4.5 Examples of the generalized regression estimatorChapter 5 Introduction to Estimation in the Presence of Nonresponse; 5.1 General background; 5.2 Errors caused by sampling and nonresponse; Appendix: Variance and mean squared error under nonresponse; Chapter 6 Weighting of Data in the Presence of Nonresponse; 6.1 Traditional approaches to weighting; 6.2 Auxiliary vectors and auxiliary information; 6.3 The calibration approach: some terminology; 6.4 Point estimation under the calibration approach; 6.5 Calibration estimators for domains; 6.6 Comments on the calibration approach 327 $a6.7 Alternative sets of calibrated weights6.8 Properties of the calibrated weights; Chapter 7 Examples of Calibration Estimators; 7.1 Examples of familiar estimators for data with nonresponse; 7.2 The simplest auxiliary vector; 7.3 One-way classi.cation; 7.4 A single quantitative auxiliary variable; 7.5 One-way classi.cation combined with a quantitative variable; 7.6 Two-way classi.cation; 7.7 A Monte Carlo simulation study; Chapter 8 The Combined Use of Sample Information and Population Information; 8.1 Options for the combined use of information 327 $a8.2 An example of calibration with information at both levels8.3 A Monte Carlo simulation study of alternative calibration procedures; 8.4 Two-step procedures in practice; Chapter 9 Analysing the Bias due to Nonresponse; 9.1 Simple estimators and their nonresponse bias; 9.2 Finding an ef.cient grouping; 9.3 Further illustrations of the nonresponse; 9.4 A general expression for the bias of the calibration estimator; 9.5 Conditions for near-unbiasedness; 9.6 A review of concepts, terms and ideas; Appendix: Proof of Proposition 9.1; Chapter 10 Selecting the Most Relevant Auxiliary Information 327 $a10.1 Discussion10.2 Guidelines for the construction of an auxiliary vector; 10.3 The prospects for near-zero bias with traditional estimators; 10.4 Further avenues towards a zero bias; 10.5 A further tool for reducing the bias; 10.6 The search for a powerful auxiliary vector; 10.7 Empirical illustrations of the indicators; 10.8 Literature review; Chapter 11 Variance and Variance Estimation; 11.1 Variance estimation for the calibration estimator; 11.2 An estimator for ideal conditions; 11.3 A useful relationship; 11.4 Variance estimation for the two-step A and two-step B procedures 327 $a11.5 A simulation study of the variance estimation technique 330 $aAround the world a multitude of surveys are conducted every day, on a variety of subjects, and consequently surveys have become an accepted part of modern life. However, in recent years survey estimates have been increasingly affected by rising trends in nonresponse, with loss of accuracy as an undesirable result. Whilst it is possible to reduce nonresponse to some degree, it cannot be completely eliminated. Estimation techniques that account systematically for nonresponse and at the same time succeed in delivering acceptable accuracy are much needed. Estimation in Surveys with Nonrespons 410 0$aWiley Series in Survey Methodology 606 $aEstimation theory 606 $aSampling (Statistics) 606 $aNonresponse (Statistics) 608 $aElectronic books. 615 0$aEstimation theory. 615 0$aSampling (Statistics) 615 0$aNonresponse (Statistics) 676 $a001.433 676 $a519.5/44 676 $a519.544 700 $aSa?rndal$b Carl-Erik$f1937-$0102999 701 $aLundstro?m$b Sixten$0614447 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143689003321 996 $aEstimation in surveys with nonresponse$91130860 997 $aUNINA