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An accuracy measure (mean squared error, MSE) is necessary when small area estimators of linear parameters are provided. Even in the case when such estimators arise from the assumption of relatively simple models for the variable of interest, as linear mixed models, the analytic form of the MSE is not suitable to be calculated explicitly. Some good and… (More)
The number of fires in forest areas of Galicia (north-west of Spain) during the summer period is quite high. Local authorities are interested in analyzing the factors that explain this phenomenon. Poisson regression models are good tools for describing and predicting the number of fires per forest areas. This work employs area-level Poisson mixed models for… (More)
A Multivariate Fay-Herriot model is used to aid the prediction of small area parameters of dependent variables with sample data aggregated to area level. The empirical best linear unbiased predictor of the parameter vector is used, and an approximation of the elements of the mean cross product error matrix is obtained by an extension of the results of… (More)
We describe a bootstrap method to estimate the bias, the variance and the distribution of the nonparametric Chambers-Dunstan estimator prediction error in finite populations. Boots-trapping is based on a bootstrap population constructed by sampling the empirical distribution of the superpopulation model recentred residuals following a smoothing process. The… (More)
Description mme fit Gaussian Multinomial mixed-effects models for small area estimation: Model 1, with one random effect in each category of the response variable; Model 2, introducing independent time effect; Model 3, introducing correlated time effect. mme calculates analytical and parametric bootstrap MSE estimators.