Abstract
In many clinical settings, it is of interest to monitor a bio-marker over time for a patient in order to identify or predict clinically important features. For example, in reproductive studies that involve basal body temperature, a low, high point or sudden changes on the trajectory have important clinical significance in determining the day of ovulation or in causing dysfunctional cycles. It is common to have patient databases with a huge quantity of data and patient information is characterised with cycles that have sparse observations. If the main interest is to make predictions, it is crucial to borrow information across cycles and among patients. In this paper, we propose the use of fast and efficient algorithms that rely on spareness-favouring hierarchical priors for P-spline basis coefficients to aid estimation of functional trajectories. Using the basal body temperature data, we present an application of the Relevant Vector Machine method that generates sparse functional linear and linear mixed models that can be used to rapidly estimate individual-specific and population average functions.
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Ciera, J.M. (2010). Fast Bayesian functional data analysis of basal body temperature. In: Mantovan, P., Secchi, P. (eds) Complex Data Modeling and Computationally Intensive Statistical Methods. Contributions to Statistics. Springer, Milano. https://doi.org/10.1007/978-88-470-1386-5_6
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DOI: https://doi.org/10.1007/978-88-470-1386-5_6
Publisher Name: Springer, Milano
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