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A BAYESIAN ANALYSIS FOR ROBUST PARAMETER DESIGNS WITH ORDINAL RESPONSES

I‐Tang Yu、Wei‐Chen Tu

2016中國統計學報Computer Science被引 1

摘要

In this work, we propose a Bayesian method to analyze robust parameter design experiments with ordinal responses. Instead of generalized linear models, we use a Gaussian process model on the latent variable representation of cumulative regression models. The optimization is then implemented based on the Bayesian predictive approach. The Bayesian Gaussian process model approach is illustrated by analyzing the foam experiment. From the analysis, we conclude that our approach can obtain a more reliable result than generalized-linear-model based approaches when complex interactions are present.

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