Small area estimation of the mean using non-parametric M-quantile regression: a comparison when a linear mixed model does not hold

DOI10.1080/00949650903575237zbMath1285.62012OpenAlexW2122468363MaRDI QIDQ3087826

Nicola Salvati, Monica Pratesi, Maria Giovanna Ranalli

Publication date: 17 August 2011

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949650903575237

zbMATH Keywords

simulation studynonparametric regressionrobust regressionpenalized splinesM-quantile regressionunit-level models

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric robustness (62G35) Sampling theory, sample surveys (62D05)

Related Items (6)

Small area mean estimation after effect clustering ⋮ Small area estimation under a semi‐parametric covariate measured with error ⋮ Estimation and Testing in M‐quantile Regression with Applications to Small Area Estimation ⋮ Functional Mixed Effects Model for Small Area Estimation ⋮ Non‐parametric generalized linear mixed models in small area estimation ⋮ Nonparametric Model-Based Estimators for the Cumulative Distribution Function of a Right Censored Variable in a Small Area

Uses Software

• SemiPar

Cites Work

• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• Case studies in environmental statistics
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• NonparametricM-quantile regression using penalised splines
• Non-Parametric Small Area Estimation Using Penalized Spline Regression
• Finding Groups in Data
• Semiparametric Regression
• Estimating distribution functions from survey data
• M-quantile models for small area estimation
• The Estimation of the Mean Squared Error of Small-Area Estimators
• Robust Statistics

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