Estimation in surveys with nonresponse [[electronic resource] /] / Carl-Erik Särndal, Sixten Lundström

• Record Nr.: UNINA9910143689003321
• Autore: Särndal Carl-Erik <1937->
• Pubbl/distr/stampa: Hoboken, NJ, : Wiley, c2005
• ISBN: 1-280-27623-1; 9786610276233; 0-470-01135-1; 0-470-01134-3
• Descrizione fisica: 1 online resource (214 p.)
• Collana: Wiley Series in Survey Methodology
• Altri autori (Persone): LundströmSixten
• Disciplina: 001.433; 519.5/44; 519.544
• Soggetti: Estimation theory; Sampling (Statistics); Nonresponse (Statistics); Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references (p. [191]-194) and index.
• Nota di contenuto: Estimation 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; 4.5 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; 6.7 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; 8.2 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; 10.1 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; 11.5 A simulation study of the variance estimation technique
• Sommario/riassunto: Around 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