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Sequential regression measurement error models with application

Moffatt, Joanne L. and Scarf, Phil 2016. Sequential regression measurement error models with application. Statistical Modelling 16 (6) , pp. 454-476. 10.1177/1471082X16663065

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Abstract

Sequential regression approaches can be used to analyze processes in which covariates are revealed in stages. Such processes occur widely, with examples including medical intervention, sports contests and political campaigns. The naïve sequential approach involves fitting regression models using the covariates revealed by the end of the current stage, but this is only practical if the number of covariates is not too large. An alternative approach is to incorporate the score (linear predictor) from the model developed at the previous stage as a covariate at the current stage. This score takes into account the history of the process prior to the stage under consideration. However, the score is a function of fitted parameter estimates and, therefore, contains measurement error. In this article, we propose a novel technique to account for error in the score. The approach is demonstrated with application to the sprint event in track cycling and is shown to reduce bias in the estimated effect of the score and avoid unrealistically extreme predictions.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Additional Information: This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http://www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage)
Publisher: SAGE Publications (UK and US)
ISSN: 1471-082X
Funders: EPSRC
Date of First Compliant Deposit: 7 December 2020
Date of Acceptance: 1 July 2016
Last Modified: 09 Dec 2020 14:45
URI: https://orca.cardiff.ac.uk/id/eprint/136867

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