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An Outlier Robust Block Bootstrap For Small Area Estimation, Payam Mokhtarian, Ray Chambers
An Outlier Robust Block Bootstrap For Small Area Estimation, Payam Mokhtarian, Ray Chambers
Payam Mokhtarian
Small area inference based on mixed models, i.e. models that contain both fixed and random effects, are the industry standard for this field, allowing between area heterogeneity to be represented by random area effects. Use of the linear mixed model is ubiquitous in this context, with maximum likelihood, or its close relative, REML, the standard method for estimating the parameters of this model. These parameter estimates, and in particular the resulting predicted values of the random area effects, are then used to construct empirical best linear unbiased predictors (EBLUPs) of the unknown small area means. It is now well known …
An Outlier Robust Block Bootstrap For Small Area Estimation, Payam Mokhtarian, Ray Chambers
An Outlier Robust Block Bootstrap For Small Area Estimation, Payam Mokhtarian, Ray Chambers
Payam Mokhtarian
Small area inference based on mixed models, i.e. models that contain both fixed and random effects, are the industry standard for this field, allowing between area heterogeneity to be represented by random area effects. Use of the linear mixed model is ubiquitous in this context, with maximum likelihood, or its close relative, REML, the standard method for estimating the parameters of this model. These parameter estimates, and in particular the resulting predicted values of the random area effects, are then used to construct empirical best linear unbiased predictors (EBLUPs) of the unknown small area means. It is now well known …