Robust designs for dose–response studies: Model and labelling robustness

Douglas P. Wiens

Computational Statistics & Data Analysis, 2021, vol. 158, issue C

Abstract: Methods for the construction of dose–response designs are presented that are robust against possible model misspecifications and mislabelled responses. The asymptotic properties are studied, leading to asymptotically minimax designs that minimize the maximum – over neighbourhoods of both types of model inadequacies – value of the mean squared error of the predictions. Both sequential and adaptive approaches are studied. Finite sample simulations and examples illustrate the gains to be made by adaptivity.

Keywords: Adaptive; Logistic; Menarche; Minimax; Probit; Sequential (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947321000232
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:158:y:2021:i:c:s0167947321000232

DOI: 10.1016/j.csda.2021.107189

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().