Bayesian Analysis of Autocorrelated Ordered Categorical Data for Industrial Quality Monitoring

Presently available methods to analyze the link between explanatory variables and an ordered categorical response implicitly assume independence. This hypothesis is no longer valid when data are collected over time. We assume temporal dependence and introduce autocorrelation using a latent-variable formulation. Due to intractable distributions, we resort to Gibbs sampling for statistical inference within the Bayesian paradigm. Variable selection is also addressed and appears as a straightforward byproduct of this framework. We illustrate the method by analyzing on-line quality data that possess such autocorrelation.

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