Sequential regression measurement error models with application. (December 2016)

• Record Type: Journal Article
• Authors: Moffatt, Joanne L; Scarf, Phil
• Is Part Of: Statistical modelling. Volume 16:Number 6(2016)
• Page Start: 454
• Page End: 476
• Publication Date: 2016-12
• Subjects: logistic regression -- measurement error -- sequential regression -- staged processes -- track cycling; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X16663065
• Languages: English
• ISSNs: 1471-082X

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.