Calibration Based On Principal Components
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
This study is concerned in reducing high dimensionality problem of auxiliary variables in the calibration estimation with the presence of nonresponse. The calibration estimation is a weighting method assists to compensate for the nonresponse in the survey analysis. Calibration estimation using principal components (PCs) is new idea in the literatures. Principal component analysis (PCA) is used in reduction dimension of the auxiliary variables. PCA in calibration estimation is presented as an alternative method for choosing the auxiliary variables. In this study, simulation on the real data is used and nonresponse mechanism is applied on the sampled data. The calibration estimator is compared using different criteria such as varying the nonresponse rate and increasing the sample size. From the results, although the calibration estimation based on the principal components have reasonable outputs to use instead of the whole auxiliary variables for the means, the variance is very large compared with based on original auxiliary variables. Finally, we identified the principal component analysis is not efficient in the reduction of high dimensionality problem of auxiliary variables in the calibration estimation for large sample sizes.
Place, publisher, year, edition, pages
2012. , 48 p.
Calibration estimator, Nonresponse, Principal components, Principal component regression, Indicators
Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:oru:diva-26582ISRN: ORU-HHS/STA-AS-2013/0007--SEOAI: oai:DiVA.org:oru-26582DiVA: diva2:574097
Subject / course
, Örebro (English)
UppsokSocial and Behavioural Science, Law
Laitila, Thomas, Professor
Karlsson, Sune, Professor