Approaches to the multivariate random variables associated with stochastic processes
A common method for obtaining moments of the residence time is using the coefficient matrix, however it has a limitation in obtaining high order moments and moments for combined compartments in a system.
In this dissertation, we first derive the bivariate moment generating function of the residence time distribution for stochastic two-compartment models with general lifetimes. It provides any order of moments and also enables us to approximate the density of the residence time using the saddlepoint approximation. The approximation method is applied to various situations including the approximation of the bivariate distribution of residence times in two-compartment models or approximations based on the truncated moment generating function.
Special attention is given to the distribution of the residence time for multi-compartment semi-Markov models. The cofactor rule and the analytic approach to the two-compartment model facilitate the derivation of the moment generating function. The properties from the embedded Markov chain are also used to extend the application of the approach.
This approach provides a complete specification of the residence time distribution based on the moment generating function and thus provides an easier calculation of high-order moments than the approach using the coefficient matrix.
Applications to drug kinetics demonstrate the simplicity and usefulness of this approach.
Advisor:Matis, James H.; Wehrly, Thomas E.; Dahm, Paul F.; Grant, William E.
School:Texas A&M University
School Location:USA - Texas
Source Type:Master's Thesis
Keywords:residence time two compartment model multi saddlepoint approximation cofactor rule
Date of Publication:08/01/2003