Nonparametric identification of population models via Gaussian processes

• Published in: Automatica (Oxford) Vol. 43; no. 7; pp. 1134 - 1144
• Main Authors: Neve, M., De Nicolao, G., Marchesi, L.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Oxford Elsevier Ltd 01.07.2007; Elsevier
• Subjects: Applied sciences; Biological and medical sciences; Computer science; control theory; systems; Control theory. Systems; Estimation theory; Exact sciences and technology; General pharmacology; Medical sciences; Modelling and identification; Neural networks; Nonparametric identification; Pharmacokinetic data; Pharmacokinetics. Pharmacogenetics. Drug-receptor interactions; Pharmacology. Drug treatments; Regularization; Splines; Splines; Neural networks; Regularization; Nonparametric identification; Estimation theory; Pharmacokinetic data; Drug; Non parametric estimation; Wiener process; Neural network; Stochastic process; Modeling; Spline approximation; Cubic spline; Gaussian process; Model matching; Pulse response; Population dynamics; System identification; Pharmacokinetics
• ISSN: 0005-1098; 1873-2836
• DOI: 10.1016/j.automatica.2006.12.024

Population models are used to describe the dynamics of different subjects belonging to a population and play an important role in drug pharmacokinetics. A nonparametric identification scheme is proposed in which both the average impulse response of the population and the individual ones are modelled as Gaussian stochastic processes. Assuming that the average curve is an integrated Wiener process, it is shown that its estimate is a cubic spline. An empirical Bayes algorithm for estimating both the average and the individual curves is worked out. The model is tested on simulated data sets as well as on xenobiotics pharmacokinetic data.