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/9780198566625.003.0009
- Subject:
- Mathematics, Probability / Statistics
Robust estimation of the prediction variance discusses the issues that arise when model misspecification is second order. That is, when the second order moments of the working model for the ...
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Robust estimation of the prediction variance discusses the issues that arise when model misspecification is second order. That is, when the second order moments of the working model for the population are incorrect, as is typically the case. Here balanced sampling is of no avail, and alternative, more robust, methods of prediction variance must be used. This chapter focuses on development of these methods for the case where the working population model is the ratio model, as well as when a general linear predictor is used and the working model has quite general first and second order moments. The case of a clustered population with unknown within cluster heteroskedasticity is also discussed and the ultimate cluster variance estimator derived.Less
Robust estimation of the prediction variance discusses the issues that arise when model misspecification is second order. That is, when the second order moments of the working model for the population are incorrect, as is typically the case. Here balanced sampling is of no avail, and alternative, more robust, methods of prediction variance must be used. This chapter focuses on development of these methods for the case where the working population model is the ratio model, as well as when a general linear predictor is used and the working model has quite general first and second order moments. The case of a clustered population with unknown within cluster heteroskedasticity is also discussed and the ultimate cluster variance estimator derived.
