Uncertainty Under A Multivariate Nested-Error Regression Model With Logarithmic Transformation
Assuming a multivariate linear regression model with one random factor, we consider the parameters defined as exponentials of mixed effects, i.e., linear combinations of fixed and random effects. Such parameters are of particular interest in prediction problems where the dependent variable is the logarithm of the variable that is the object of inference. We derive bias-corrected empirical predictors of such parameters. A second order approximation for the mean crossed product error of the predictors of two of these parameters is obtained, and an estimator is derived from it. The mean squared error is obtained as a particular case.
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- Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 15(1), pages 1-96, June.
- Amparo Baillo & Isabel Molina, 2005. "Mean Squared Errors Of Small Area Estimators Under A Unit-Level Multivariate Model," Statistics and Econometrics Working Papers ws054007, Universidad Carlos III, Departamento de Estadística y Econometría.
- Eric V. Slud & Tapabrata Maiti, 2006. "Mean-squared error estimation in transformed Fay-Herriot models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 239-257.
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