Spatial extreme learning machines: An application on prediction of disease counts. (September 2019)

• Record Type: Journal Article
• Authors: Prates, Marcos O
• Is Part Of: Statistical methods in medical research. Volume 28:Number 9(2019)
• Page Start: 2583
• Page End: 2594
• Publication Date: 2019-09
• Subjects: Bayesian method -- extreme learning machines -- integrated nested Laplace approximation -- missing data -- spatial modeling; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280218767985
• Languages: English
• ISSNs: 0962-2802

Abstract

Extreme learning machines have gained a lot of attention by the machine learning community because of its interesting properties and computational advantages. With the increase in collection of information nowadays, many sources of data have missing information making statistical analysis harder or unfeasible. In this paper, we present a new model, coined spatial extreme learning machine, that combine spatial modeling with extreme learning machines keeping the nice properties of both methodologies and making it very flexible and robust. As explained throughout the text, the spatial extreme learning machines have many advantages in comparison with the traditional extreme learning machines. By a simulation study and a real data analysis we present how the spatial extreme learning machine can be used to improve imputation of missing data and uncertainty prediction estimation.