Erschienen in:
Biometrics, 44 (1988) 3, Seite 673-683
Sprache:
Englisch
ISSN:
0006-341X;
1541-0420
Entstehung:
Anmerkungen:
Beschreibung:
<p>A stochastic approximation algorithm is proposed for recursive estimation of the hyperparameters characterizing, in a population, the probability density function of the parameters of a statistical model. For a given population model defined by a parametric model of a biological process, an error model, and a class of densities on the set of the individual parameters, this algorithm provides a sequence of estimates from a sequence of individuals' observation vectors. Convergence conditions are verified for a class of population models including usual pharmacokinetic applications. This method is implemented for estimation of pharmacokinetic population parameters from drug multiple-dosing data. Its estimation capabilities are evaluated and compared to a classical method in population pharmacokinetics, the first-order method (NONMEM), on simulated data.</p>