Calibration Adjustment for Nonresponse in Sample Surveys
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
In this thesis, we discuss calibration estimation in the presence of nonresponse with a focus on the linear calibration estimator and the propensity calibration estimator, along with the use of different levels of auxiliary information, that is, sample and population levels. This is a fourpapers- based thesis, two of which discuss estimation in two steps. The two-step-type estimator here suggested is an improved compromise of both the linear calibration and the propensity calibration estimators mentioned above. Assuming that the functional form of the response model is known, it is estimated in the first step using calibration approach. In the second step the linear calibration estimator is constructed replacing the design weights by products of these with the inverse of the estimated response probabilities in the first step. The first step of estimation uses sample level of auxiliary information and we demonstrate that this results in more efficient estimated response probabilities than using population-level as earlier suggested. The variance expression for the two-step estimator is derived and an estimator of this is suggested. Two other papers address the use of auxiliary variables in estimation. One of which introduces the use of principal components theory in the calibration for nonresponse adjustment and suggests a selection of components using a theory of canonical correlation. Principal components are used as a mean to accounting the problem of estimation in presence of large sets of candidate auxiliary variables. In addition to the use of auxiliary variables, the last paper also discusses the use of explicit models representing the true response behavior. Usually simple models such as logistic, probit, linear or log-linear are used for this purpose. However, given a possible complexity on the structure of the true response probability, it may raise a question whether these simple models are effective. We use an example of telephone-based survey data collection process and demonstrate that the logistic model is generally not appropriate.
Place, publisher, year, edition, pages
Örebro: Örebro university , 2016. , 9 p.
Örebro Studies in Statistics, ISSN 1651-8608 ; 8
Auxiliary variables, Calibration, Nonresponse, principal com-ponents, regression estimator, response probability, survey sampling, two-step estimator, variance estimator, weighting
Probability Theory and Statistics
Research subject Statistics
IdentifiersURN: urn:nbn:se:oru:diva-51966ISBN: 978-91-7529-160-4 (print)OAI: oai:DiVA.org:oru-51966DiVA: diva2:957897
2016-10-27, Örebro universitet, Musikhögskolan, Hörsalen, Fakultetsgatan 1, Örebro, 14:15 (Swedish)
Lehtonen, Risto, Professor
Laitila, Thomas, ProfessorKarlsson, Sune, Professor
List of papers