Data-driven transformations in small area estimation
| Autor: | Sören Pannier, Nikos Tzavidis, Timo Schmid, Natalia Rojas-Perilla |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
Statistics and Probability Mixed model Economics and Econometrics small area estimation Mean squared error MSE estimation Small area estimation Computer science Gaussian MSE es- timation 010603 evolutionary biology 01 natural sciences Data-driven 010104 statistics & probability symbols.namesake poverty mapping maximum likelihood theory ddc:330 0101 mathematics Parametric statistics 300 Sozialwissenschaften::310 Statistiken Random effects model linear mixed regression model Transformation (function) data-driven transformations symbols Statistics Probability and Uncertainty Algorithm Social Sciences (miscellaneous) |
| Popis: | Summary Small area models typically depend on the validity of model assumptions. For example, a commonly used version of the empirical best predictor relies on the Gaussian assumptions of the error terms of the linear mixed regression model: a feature rarely observed in applications with real data. The paper tackles the potential lack of validity of the model assumptions by using data-driven scaled transformations as opposed to ad hoc chosen transformations. Different types of transformations are explored, the estimation of the transformation parameters is studied in detail under the linear mixed regression model and transformations are used in small area prediction of linear and non-linear parameters. The use of scaled transformations is crucial as it enables fitting the linear mixed regression model with standard software and hence it simplifies the work of the data analyst. Mean-squared error estimation that accounts for the uncertainty due to the estimation of the transformation parameters is explored by using the parametric and semiparametric (wild) bootstrap. The methods proposed are illustrated by using real survey and census data for estimating income deprivation parameters for municipalities in the Mexican state of Guerrero. Simulation studies and the results from the application show that using carefully selected, data-driven transformations can improve small area estimation. |
| Databáze: | OpenAIRE |
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