Statistical analysis of small-area data based on independence, spatial, non-hierarchical, and hierarchical models

DOI10.1016/j.csda.2008.07.033zbMath1453.62123OpenAlexW1973052158WikidataQ104697307 ScholiaQ104697307MaRDI QIDQ961742

Emily L. Kang, Noel Cressie, De-Sheng Liu

Publication date: 1 April 2010

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

Full work available at URL: https://doi.org/10.1016/j.csda.2008.07.033

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Inference from spatial processes (62M30)

Related Items (10)

Special issue on small area estimation ⋮ Bayesian nonstationary spatial modeling for very large datasets ⋮ Estimation of spatial autoregressive models with measurement error for large data sets ⋮ Spatial Statistical Data Fusion for Remote Sensing Applications ⋮ Parallel inference for massive distributed spatial data using low-rank models ⋮ Editorial. Spatial statistics: methods, models \& computation ⋮ Small area estimation with spatial similarity ⋮ Using temporal variability to improve spatial mapping with application to satellite data ⋮ Spatio-temporal modeling of sudden infant death syndrome data ⋮ A case study competition among methods for analyzing large spatial data

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Likelihood-based estimation for Gaussian MRFs
• Estimating the dimension of a model
• M-estimation in the presence of unequal scale
• Prediction diagnostics for spatial linear models
• Bayesian inference for variance components using only error contrasts
• Doctors’ Prescribing Patterns in the Midi-Pyrénées rRegion of France: Point-process Aggregation

This page was built for publication: Statistical analysis of small-area data based on independence, spatial, non-hierarchical, and hierarchical models