Resistance to Outliers of M-Quantile and Robust Random Effects Small Area Models

DOI10.1080/03610918.2012.707724zbMath1291.62107OpenAlexW1988698734MaRDI QIDQ5415869

Monica Pratesi, Caterina Giusti, Nicola Salvati, Nikos Tzavidis

Publication date: 19 May 2014

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2012.707724

zbMATH Keywords

maximum likelihood estimationinfluence functionweighted least squaresmultilevel modelM-estimation

Mathematics Subject Classification ID

Nonparametric robustness (62G35) Order statistics; empirical distribution functions (62G30)

Related Items (3)

Parametric bootstrap mean squared error of a small area multivariate EBLUP ⋮ Small area estimation based on M-quantile models in presence of outliers in auxiliary variables ⋮ Estimation and Testing in M‐quantile Regression with Applications to Small Area Estimation

Cites Work

• Non-parametric bootstrap mean squared error estimation for \(M\)-quantile estimators of small area averages, quantiles and poverty indicators
• ROBUST ESTIMATION OF SMALL-AREA MEANS AND QUANTILES
• On Parametric Bootstrap Methods for Small Area Prediction
• Robust small area estimation
• Regression Quantiles
• Bias-calibrated Estimation from Sample Surveys Containing Outliers
• Robust Restricted Maximum Likelihood in Mixed Linear Models
• Estimating distribution functions from survey data
• Bootstrapping the Dorfman–Hall–Chambers–Dunstan estimator of a finite population distribution function
• M-quantile models for small area estimation

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