Date of Award
Master of Science
Wei Wei, Peter Hinow
There is the saying which says you cannot see the woods for the trees. This
thesis aims to circumvent this unfortunate situation: Longitudinal data on
tree growth, as an example of multiple observations of similar individuals
pooled together in one data set, are modeled simultaneously rather than
each individual separately. This is done under the assumption that one
model is suitable for all individuals but its parameters vary following un-
known nonparametric random effect distributions. The goal is a maximum
likelihood estimation of these distributions considering all provided data and
using basis-spline-approximations for the densities of each distribution func-
tion over the same spline-base. The implementation of all procedures is
carried out in R and attached to this thesis.
Stenz, Hartmut Jakob, "Longitudinal Data Models with Nonparametric Random Effect Distributions" (2016). Theses and Dissertations. 1207.