Small area estimation under spatial nonstationarity

DOI10.1016/j.csda.2012.02.006zbMath1255.62330OpenAlexW2255868970MaRDI QIDQ1927071

Nikos Tzavidis, Hukum Chandra, Nicola Salvati, Ray Chambers

Publication date: 30 December 2012

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1070&context=cssmwp

zbMATH Keywords

spatial analysisborrowing strength over spacegeographical weighted regressionout of sample small area estimation

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Applications of statistics to environmental and related topics (62P12) Geostatistics (86A32)

Related Items (11)

Small area mean estimation after effect clustering ⋮ Small area estimation under area-level generalized linear mixed models ⋮ Small area estimation combining data from two surveys ⋮ Special issue on small area estimation ⋮ Robust Small Area Estimation under Spatial Non‐stationarity ⋮ Small area estimation with spatially varying natural exponential families ⋮ Small area estimation of general finite-population parameters based on grouped data ⋮ Geographically weighted regression model-assisted estimation in survey sampling ⋮ Spatial robust small area estimation ⋮ Outlier Robust Small-Area Estimation Under Spatial Correlation ⋮ Small area estimation of expenditure means and ratios under a unit-level bivariate linear mixed model

Uses Software

• R
• FRK

Cites Work

• Unnamed Item
• Spline smoothing in small area trend estimation and forecasting
• Small area estimation: the EBLUP estimator based on spatially correlated random area effects
• Small area estimation via M-quantile geographically weighted regression
• Small area estimation using a nonparametric model-based direct estimator
• Fixed Rank Kriging for Very Large Spatial Data Sets
• Non-Parametric Small Area Estimation Using Penalized Spline Regression
• Best Linear Unbiased Estimation and Prediction under a Selection Model
• Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
• M-quantile models for small area estimation
• A Simplex Method for Function Minimization
• The Estimation of the Mean Squared Error of Small-Area Estimators

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