Canadian Journal of Statistics

Robust design for the estimation of a threshold probability

Journal Article


We consider the construction of robust sampling designs for the estimation of threshold probabilities in spatial studies. A threshold probability is a probability that the value of a stochastic process at a particular location exceeds a given threshold. We propose designs such that the estimation of threshold probabilities is robust to two possible model uncertainties: misspecified regression responses and covariance structures. To address these two uncertainties of this stochastic process, we average the mean squared error of the predicted values relative to the true values, over all possible covariance structures in a neighbourhood of the experimenter's nominal choice, and then maximize it over a neighbourhood of the fitted model. Finally, the maximum is minimized to obtain the robust designs. The Canadian Journal of Statistics 46: 470–481; 2018 © 2018 Statistical Society of Canada

Related Topics

Related Publications

Related Content

Site Footer


This website is provided by John Wiley & Sons Limited, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ (Company No: 00641132, VAT No: 376766987)

Published features on are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.