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We develop a Bayesian nonparametric framework for modeling ordinal regressionrelationships which evolve in discrete time. The motivating applicationinvolves a key problem in fisheries research on estimating dynamically evolvingrelationships between age, length and maturity, the latter recorded on anordinal scale. The methodology builds from nonparametric mixture modeling forthe joint stochastic mechanism of covariates and latent continuous responses.This approach yields highly flexible inference for ordinal regression functionswhile at the same time avoiding the computational challenges of parametricmodels. A novel dependent Dirichlet process prior for time-dependent mixingdistributions extends the model to the dynamic setting. The methodology is usedfor a detailed study of relationships between maturity, age, and length forChilipepper rockfish, using data collected over 15 years along the coast ofCalifornia.

Modeling for Dynamic Ordinal Regression Relationships: An Application to Estimating Maturity of Rockfish in California