Sequential regression measurement error models with application

Scarf, PA ORCID: and Moffatt, JL 2016, 'Sequential regression measurement error models with application' , Statistical Modelling, 16 (6) , pp. 454-476.

PDF - Published Version
Available under License Creative Commons Attribution.

Download (693kB) | Preview
[img] PDF - Accepted Version
Restricted to Repository staff only

Download (629kB) | Request a copy


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

Item Type: Article
Schools: Schools > Salford Business School > Salford Business School Research Centre
Journal or Publication Title: Statistical Modelling
Publisher: Arnold Publishers
ISSN: 1471-082X
Related URLs:
Funders: Engineering and Physical Sciences Research Council (EPSRC)
Depositing User: Dr Philip Scarf
Date Deposited: 01 Sep 2016 07:32
Last Modified: 15 Feb 2022 21:07

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)


Downloads per month over past year