Nonparametric Analysis of Unbalanced Paired-Comparison or Ranked Data

Bayesian nonparametric models for ranked data

We develop a Bayesian nonparametric extension of the popular Plackett-Luce choice model that can handle an infinite number of choice items. Our framework is based on the theory of random atomic measures, with the prior specified by a gamma process. We derive a posterior characterization and a simple and effective Gibbs sampler for posterior simulation. We develop a time-varying extension of our...

Nonparametric inference for unbalanced time series data

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Analysis of Ordinal Paired Comparison Data

Bayesian nonparametric models for ranked data: Supplementary Material

The marginal probability (14) is obtained by taking the expectation of (13) with respect to G. Note however that (13) is a density, so to be totally precise here we need to work with the probability of infinitesimal neighborhoods around the observations instead, which introduces significant notational complexity. To keep the notation simple, we will work with densities, leaving it to the carefu...

Nonparametric Methods for Unbalanced Multivariate Data and Many Factor Levels

We propose different nonparametric tests for multivariate data and derive their asymptotic distribution for unbalanced designs in which the number of factor levels tends to infinity (large a, small ni case). Quasi gratis, some new parametric multivariate tests suitable for the large a asymptotic case are also obtained. Finite sample performances are investigated and compared in a simulation stu...