Generalized linear mixed model with bayesian rank likelihood

Lyubov Doroshenko () and Brunero Liseo ()
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Lyubov Doroshenko: Sapienza Università di Roma
Brunero Liseo: Sapienza Università di Roma

Statistical Methods & Applications, 2023, vol. 32, issue 2, No 4, 425-446

Abstract: Abstract We consider situations where a model for an ordered categorical response variable is deemed necessary. Standard models may not be suited to perform this analysis, being that the marginal probability effects to a large extent are predetermined by the rigid parametric structure. We propose to use a rank likelihood approach in a non Gaussian framework and show how additional flexibility can be gained by modeling individual heterogeneity in terms of latent structure. This approach avoids to set a specific link between the observed categories and the latent quantities and it is discussed in the broadly general case of longitudinal data. A real data example is illustrated in the context of sovereign credit ratings modeling and forecasting.

Keywords: Ordinal data; Latent variables; Missing data; Gibbs sampler; Longitudinal data; Ratings (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10260-022-00657-y

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