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An Introduction to Model-Based Survey Sampling with Applications$
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Ray Chambers and Robert Clark

Print publication date: 2012

Print ISBN-13: 9780198566625

Published to Oxford Scholarship Online: May 2012

DOI: 10.1093/acprof:oso/9780198566625.001.0001

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Using Transformations in Sample Survey Inference

Using Transformations in Sample Survey Inference

(p.214) 17 Using Transformations in Sample Survey Inference
An Introduction to Model-Based Survey Sampling with Applications

Raymond L. Chambers

Robert G. Clark

Oxford University Press

Using transformations in sample survey inference is the final chapter of this book and describes the extension of the empirical best prediction approach to the situation where the population values of interest do not follow a linear model in their original scale of measurement, but can be transformed so that this is the case. In particular, it focuses on the situation where the logarithm of the survey variable can be modelled linearly, and develops methodology for correcting the transformation biases of empirical best predictors of the population mean. The logarithmic transformation is particularly useful when there are outliers in the data, and outlier robust versions of these predictors are developed. Empirical results based on actual business survey data are used to demonstrate the efficacy of the transformation-based predictors. Both estimation and sample design issues caused by model-misspecification in the transformed scale are also discussed.

Keywords:   prediction under transformation, logarithmic transformation, transformation bias, smearing estimation, outlier robust prediction, model misspecification, balanced sampling

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