Geostatistical estimation and prediction for censored responses

Author(s)

Ordonez, Jose A. ^[1] ; Bandyopadhyay, Dipankar ^[2] ; Lachos, Victor H. ^[3] ; Cabral, Celso R. B. ^[4]

Affiliation

^[1] Univ Estadual Campinas, Dept Stat, Campinas, SP - Brazil

^[2] Virginia Commonwealth Univ, Dept Biostat, Richmond, VA - USA

^[3] Univ Connecticut, Dept Stat, 215 Glenbrook Rd U-4120, Storrs, CT 06269 - USA

^[4] Univ Fed Amazonas, Dept Stat, Manaus, Amazonas - Brazil

• Document type: Review article
• Source: SPATIAL STATISTICS; v. 23, p. 109-123, MAR 2018.
• Web of Science Citations: 1

Abstract

Spatially-referenced geostatistical responses that are collected in environmental sciences research are often subject to detection limits, where the measures are not fully quantifiable. This leads to censoring (left, right, interval, etc.), and various ad hoc statistical methods (such as choosing arbitrary detection limits, or data augmentation) are routinely employed during subsequent statistical analysis for inference and prediction. However, inference may be imprecise and sensitive to the assumptions and approximations involved in those arbitrary choices. To circumvent this, we propose an exact maximum likelihood estimation framework of the fixed effects and variance components and related prediction via a novel application of the Stochastic Approximation of the Expectation Maximization (SAEM) algorithm, allowing for easy and elegant estimation of model parameters under censoring. Both simulation studies and application to a real dataset on arsenic concentration collected by the Michigan Department of Environmental Quality demonstrate the advantages of our method over the available naive techniques in terms of finite sample properties of the estimates, prediction, and robustness. The proposed methods can be implemented using the R package CensSpatial. (c) 2017 Elsevier B.V. All rights reserved. (AU)

• FAPESP's process: 15/20922-5 - Flexible regression modeling for censored data
• Grantee: Víctor Hugo Lachos Dávila
• Support Opportunities: Research Grants - Visiting Researcher Grant - Brazil