Sequential regression techniques with application to the individual sprint in track cycling

Moffatt, JL 2012, Sequential regression techniques with application to the individual sprint in track cycling , PhD thesis, University of Salford.

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Abstract

The research work described in this thesis is concerned with processes comprising a sequence of stages, where states and actions taken during each stage influence the outcome at the end of the process. Statistical analysis of such processes using standard approaches can be problematic due to the potentially large number of covariates that are influential, especially towards the end of the process. Therefore, three alternative statistical techniques of increasing complexity were developed. These techniques are all based on a sequential approach, in which logistic regression models are developed at consecutive stages. These techniques were applied to the individual sprint event in track cycling and all successfully gave insight into beneficial tactics for each stage of the race. The first technique involves considering for each model only covariates related to the current and previous stages. As such, a sequence of overlapping models is created. This approach successfully enabled stable and easy to interpret models to be created. However, the joint effect of applying tactics at different stages of the individual sprint could not be determined. The sequential logistic regression technique overcame this limitation by using the score (the logistic transformation of the probability of outcome) from the model developed at the previous stage as a covariate in the succeeding model. As such, all prior information can be incorporated into each model. However this score is estimated with uncertainty, which can cause the model parameter estimates to be biased. Furthermore, the effects of this intrinsic measurement error were found to propagate through stages, particularly in terms of the relative importance of prior and current states and actions. The novel third technique therefore combines the sequential logistic regression approach with measurement error techniques to account for error in the score.

Item Type:	Thesis (PhD)
Contributors:	Scarf, PA (Supervisor)
Schools:	Schools > Salford Business School
Depositing User:	Institutional Repository
Date Deposited:	30 Jul 2021 09:45
Last Modified:	04 Aug 2022 11:23
URI:	https://usir.salford.ac.uk/id/eprint/61361

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