Partitioning around medoids clustering and random forest classification for GIS-informed imputation of fluoride concentration data

DOI10.1214/21-AOAS1516zbMath1498.62008OpenAlexW4220729913WikidataQ112271265 ScholiaQ112271265MaRDI QIDQ2135383

Jeannie Ginnis, Poojan Shrestha, Yu Gu, Molina Shah, Kimon Divaris, Miguel A. Simancas-Pallares, Donglin Zeng, John S. Preisser

Publication date: 6 May 2022

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/21-aoas1516

zbMATH Keywords

clusteringmultiple imputationspatial interpolationmissing valuesrandom forest

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Missing data (62D10)

Uses Software

• Silhouettes
• cluster (R)
• ArcGIS
• rpart
• MICE
• randomForest
• clusfind

Cites Work

• Bagging predictors
• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• General variational approach to the interpolation problem
• Bias in 2-part mixed models for longitudinal semicontinuous data
• Scattered Data Interpolation: Tests of Some Method
• Statistics for Spatial Data
• NOTES ON CONTINUOUS STOCHASTIC PHENOMENA
• Random forests
• Unnamed Item
• Unnamed Item
• Unnamed Item

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