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Smooth random change point models.

van den Hout, A; Muniz-Terrera, G; Matthews, FE; (2011) Smooth random change point models. Stat Med , 30 (6) 599 - 610. 10.1002/sim.4127.

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Abstract

Change point models are used to describe processes over time that show a change in direction. An example of such a process is cognitive ability, where a decline a few years before death is sometimes observed. A broken-stick model consists of two linear parts and a breakpoint where the two lines intersect. Alternatively, models can be formulated that imply a smooth change between the two linear parts. Change point models can be extended by adding random effects to account for variability between subjects. A new smooth change point model is introduced and examples are presented that show how change point models can be estimated using functions in R for mixed-effects models. The Bayesian inference using WinBUGS is also discussed. The methods are illustrated using data from a population-based longitudinal study of ageing, the Cambridge City over 75 Cohort Study. The aim is to identify how many years before death individuals experience a change in the rate of decline of their cognitive ability.

Type:Article
Title:Smooth random change point models.
Location:England
DOI:10.1002/sim.4127
Language:English
Keywords:Aged, Aging, Bayes Theorem, Cognition, Cohort Studies, Data Interpretation, Statistical, Humans, Likelihood Functions, Longitudinal Studies, Models, Statistical, Stochastic Processes
UCL classification:UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science

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