Sequential regression measurement error models with application

Scarf, PA; Moffatt, JL

Abstract

Sequential regression approaches can be used to analyse processes in which covariates are revealed in stages. Such processes occur widely, with examples including medical intervention, sports contests, and political campaigns. The naïvenaive 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 paper, 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

Citation

Scarf, P., & Moffatt, J. (2016). Sequential regression measurement error models with application. Statistical Modelling, 16(6), 454-476. https://doi.org/10.1177/1471082X16663065

• Journal Article Type: Article
• Acceptance Date: Jun 28, 2016
• Online Publication Date: Oct 10, 2016
• Publication Date: Dec 1, 2016
• Deposit Date: Sep 1, 2016
• Publicly Available Date: Sep 15, 2016
• Journal: Statistical Modelling
• Print ISSN: 1471-082X
• Electronic ISSN: 1477-0342
• Publisher: SAGE Publications
• Volume: 16
• Issue: 6
• Pages: 454-476
• DOI: https://doi.org/10.1177/1471082X16663065
• Publisher URL: http://dx.doi.org/10.1177/1471082X16663065
• Related Public URLs: https://uk.sagepub.com/en-gb/eur/statistical-modelling/journal201837