Binary Time Series Modeling with
Application to Adhesion Frequency Experiments
Cell adhesion plays an important role in many physiological and
pathological processes. The only published method for measuring the kinetic
rates of cell adhesion is the repeated adhesion frequency. Traditional analysis
of adhesion frequency experiments assumes that the adhesion test cycles are
independent Bernoulli trials. This assumption is often violated in practice.
The major part of the talk will focus on a new binary time series model
motivated by the analysis of repeated adhesion frequency tests. To assess the
adequacy of distribution assumptions on the dependent binary data with random
effects, a goodness-of-fit statistic will be proposed. The asymptotic
distribution of the goodness-of-fit statistic is derived and its finite-sample
performance is examined via a simulation study. Application of the proposed
methodology to real data from a T-cell experiment at Georgia Tech provides some
valuable information, including quantifying the memory effects in cells and molecules. This information is crucial
to the body's defense in the immune system.
The second part of
the talk shall briefly touch upon my new work in computer experiments with
branching and nested factors. In many experiments, some of the factors exist
only within the level of another factor. Such factors are often called nested
factors. A factor within which other factors are nested is called a branching
factor. Design and analysis of
experiments with branching and nested factors are challenging and have not
received much attention in the literature. Motivated by a computer experiment
in a machining process, we develop optimal Latin hypercube designs and kriging
methods that can accommodate branching and nested factors. Through the
application of the proposed methods, optimal machining conditions and tool edge
geometry are attained, which resulted in a remarkable improvement in the
machining process.