Static and Dynamic Bart for Rank-Order Data

Ranking lists are often provided at regular time intervals in a range of applications, including economics, sports, marketing, and politics. Most popular methods for rank-order data postulate a linear specification for the latent scores, which determine the observed ranks, and ignore the temporal dependence of the ranking lists. To address these issues, novel nonparametric static (ROBART) and autoregressive (ARROBART) models are developed, with latent scores defined as nonlinear Bayesian additive regression tree functions of covariates.To make inferences in the dynamic ARROBART model, closed-form filtering, predictive, and smoothing distributions for the latent time-varying scores are derived. These results are applied in a Gibbs sampler with data augmentation for posterior inference.The proposed methods are shown to outperform existing competitors in simulation studies, static data applications to electoral data, stated preferences for sushi and movies, and dynamic data applications to economic complexity rankings of countries and weekly pollster rankings of NCAA football teams

Autoregressive model · Bayesian probability · Data set · Gibbs sampling · Nonparametric statistics · Smoothing · Blind Source Separation Techniques · Model Reduction and Neural Networks · Seismic Imaging and Inversion Techniques

• A law of comparative judgment.

  L L Thurstone•Psychological Review•1927

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• Bart

  Hugh A Chipman, Edward I George et al.•The Annals of Applied Statistics•2010

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• A New Measure of Rank Correlation

  M G Kendall•Biometrika•1938

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• Generalized random utility model

  Open Access•Joan Walker, Joan L Walker et al.•Mathematical Social Sciences•2002

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