Parametric Models vs. Empirical Distributions and Ordinal Regression

Background In modeling data we frequently need to estimate more than a mean. We may want to estimate quantiles, dispersion, or tail probabilities. These require model assumptions to be correct when the model is a smooth parametric one. Even when estimating a mean, for inference to be accurate we need distributional assumptions we make to be satisfied. The central limit theorem offers no protection…