Classification trees for poverty mapping

DOI10.1016/j.csda.2017.05.009zbMath1466.62033OpenAlexW2620812311MaRDI QIDQ1658373

Steve Haslett, Penny Bilton, Siva Ganesh, Geoffrey Jones

Publication date: 14 August 2018

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

Full work available at URL: https://doi.org/10.1016/j.csda.2017.05.009

zbMATH Keywords

clustered datasmall area estimationcomplex survey datasustainable development goals

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Computational methods for problems pertaining to statistics (62-08) Sampling theory, sample surveys (62D05)

Related Items (1)

Regression trees for poverty mapping

Uses Software

• R
• MASS (R)
• rms
• rpart
• SDaA
• saery
• rpart.plot

Cites Work

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• Small area estimation: an appraisal. With comments and a rejoinder by the authors
• Multivariate Fay-Herriot models for small area estimation
• The jackknife and bootstrap
• Small area estimation of poverty proportions under area-level time models
• Non-parametric bootstrap mean squared error estimation for \(M\)-quantile estimators of small area averages, quantiles and poverty indicators
• Small Area Estimation
• Statistical Analysis of Financial Data in S-Plus
• Building Consistent Regression Trees From Complex Sample Data
• Resampling Inference With Complex Survey Data
• Bootstrapping Clustered Data
• Micro-Level Estimation of Poverty and Inequality
• Problems in the Analysis of Survey Data, and a Proposal
• Problems in Plane Sampling
• Introduction to variance estimation
• The elements of statistical learning. Data mining, inference, and prediction
• Random forests
• Impact of the bootstrap on sample surveys

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