The methodology of flowgraph models.
PhD thesis, The London School of Economics and Political Science (LSE).
Flowgraph models are directed graph models for describing the dynamic changes in a stochastic process. They are one class of multistate models that are applied to analyse time-to-event data. The main
motivation of the ﬂowgraph models is to determine the distribution of
the total waiting times until an event of interest occurs in a stochastic
process that progresses through various states. This thesis applies the
methodology of ﬂowgraph models to the study of Markov and SemiMarkov processes.
The underlying approach of the thesis is that the access to the moment generating function (MGF) and cumulant generating function
(CGF), provided by Mason’s rule enables us to use the Method of
Moments (MM) which depends on moments and cumulant. We give
a new derivation of the Mason’s rule to compute the total waiting
MGF based on the internode transition matrix of a ﬂowgraph. Next,
we demonstrate methods to determine and approximate the distribution of total waiting time based on the inversion of the MGF, including
an alternative approach using the Pad´e approximation of the MGF,
which always yields a closed form density.
For parameter estimation, we extend the Expectation-Maximization
(EM) algorithm to estimate parameters in the mixture of negative
weight exponential density. Our second contribution is to develop a
bias correction method in the Method of Moments (BCMM). By investigating methods for tail area approximation, we propose a new
way to estimate the total waiting time density function and survival
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