JSM 2011 Online Program

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Abstract Details

Activity Number: 470
Type: Contributed
Date/Time: Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #301013
Title: The Constrained Random Effect Models for Nanoparticle Synthesis in a Supercritical CO2 Process
Author(s): Hin Kyeol WOO*+ and Jye Chyi Lu
Companies: Georgia Tech and Georgia Tech
Address: 2202 Druid Hills Reserve Dr NE, ATLANTA, GA, 30329,
Keywords: Statistical modeling ; Computer experiment ; Generalized linear mixed model ; Variance component
Abstract:

This research develops a two-part model for observations from nano-particle fabrication experiments. Since there are certain combinations of process variables resulting to unproductive process outcomes, a logistic model is used to characterize such a process behavior. For the cases with productive outcomes a normal regression serves the second part of the model. Because the data are obtained from computer experiments, random-effects are included in both logistics and normal regression models to describe the potential spatial correlation among data. The likelihood function for this two-part model is complicated and thus the maximum likelihood estimation is intractable. This research studies approximation techniques based on Taylor series extension to simplify the likelihood. An algorithm is developed to find estimates for maximizing the approximated likelihood.


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