*Raymond L. Chambers and Robert G. Clark*

- Published in print:
- 2012
- Published Online:
- May 2012
- ISBN:
- 9780198566625
- eISBN:
- 9780191738449
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198566625.003.0008
- Subject:
- Mathematics, Probability / Statistics

Robust prediction under model misspecification focuses on the important topic of how to ensure unbiased prediction even when the assumed population model is not precisely specified. The general role ...
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Robust prediction under model misspecification focuses on the important topic of how to ensure unbiased prediction even when the assumed population model is not precisely specified. The general role of sample balance in ensuring this unbiasedness is explored in the context of the homogeneous and the ratio population models, and the problem of maintaining a suitable trade-off between prediction efficiency under a working model and unbiasedness under alternative population models is discussed. A general result that provides the necessary conditions for both unbiasedness and efficiency is provided and the extension of balanced sampling to the clustered population model is discussed. A misspecification-robust alternative to balanced sampling is flexible estimation, and the chapter concludes with a development of finite population prediction based on a non-parametric regression fit to the sample data.Less

Robust prediction under model misspecification focuses on the important topic of how to ensure unbiased prediction even when the assumed population model is not precisely specified. The general role of sample balance in ensuring this unbiasedness is explored in the context of the homogeneous and the ratio population models, and the problem of maintaining a suitable trade-off between prediction efficiency under a working model and unbiasedness under alternative population models is discussed. A general result that provides the necessary conditions for both unbiasedness and efficiency is provided and the extension of balanced sampling to the clustered population model is discussed. A misspecification-robust alternative to balanced sampling is flexible estimation, and the chapter concludes with a development of finite population prediction based on a non-parametric regression fit to the sample data.

*Raymond L. Chambers and Robert G. Clark*

- Published in print:
- 2012
- Published Online:
- May 2012
- ISBN:
- 9780198566625
- eISBN:
- 9780191738449
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198566625.003.0013
- Subject:
- Mathematics, Probability / Statistics

Estimation for multipurpose surveys considers the situation of a survey with many output variables and multiple auxiliary variables. In this context, linear estimation based on a single set of ...
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Estimation for multipurpose surveys considers the situation of a survey with many output variables and multiple auxiliary variables. In this context, linear estimation based on a single set of multipurpose sample weights represents the dominant approach in sample survey estimation. The development in this chapter includes linear calibrated weighting and ridge weighting, i.e. sample weighting based on a minimum mean squared error ridge regression fit to the survey data, with the latter approach specifically aimed at reducing the incidence of non-positive survey weights caused by sample imbalance and/or model overspecification. The extension to non-parametric regression modelling is also considered, and the important trade-off between sample balance and sample weight variability is discussed.Less

Estimation for multipurpose surveys considers the situation of a survey with many output variables and multiple auxiliary variables. In this context, linear estimation based on a single set of multipurpose sample weights represents the dominant approach in sample survey estimation. The development in this chapter includes linear calibrated weighting and ridge weighting, i.e. sample weighting based on a minimum mean squared error ridge regression fit to the survey data, with the latter approach specifically aimed at reducing the incidence of non-positive survey weights caused by sample imbalance and/or model overspecification. The extension to non-parametric regression modelling is also considered, and the important trade-off between sample balance and sample weight variability is discussed.

*Raymond L. Chambers and Robert G. Clark*

- Published in print:
- 2012
- Published Online:
- May 2012
- ISBN:
- 9780198566625
- eISBN:
- 9780191738449
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198566625.003.0017
- Subject:
- Mathematics, Probability / Statistics

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 ...
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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.Less

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.