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Semi-parametric bivariate polychotomous ordinal regression

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A pair of polychotomous random variables $$(Y_1,Y_2)^\top =:{\varvec{Y}}$$(Y1,Y2)⊤=:Y, where each $$Y_j$$Yj has a totally ordered support, is studied within a penalized generalized linear model framework. We deal with a triangular… Click to show full abstract

A pair of polychotomous random variables $$(Y_1,Y_2)^\top =:{\varvec{Y}}$$(Y1,Y2)⊤=:Y, where each $$Y_j$$Yj has a totally ordered support, is studied within a penalized generalized linear model framework. We deal with a triangular generating process for $${\varvec{Y}}$$Y, a structure that has been employed in the literature to control for the presence of residual confounding. Differently from previous works, however, the proposed model allows for a semi-parametric estimation of the covariate-response relationships. In this way, the risk of model mis-specification stemming from the imposition of fixed-order polynomial functional forms is also reduced. The proposed estimation methods and related inferential results are finally applied to study the effect of education on alcohol consumption among young adults in the UK.

Keywords: ordinal regression; polychotomous ordinal; bivariate polychotomous; model; semi parametric; parametric bivariate

Journal Title: Statistics and Computing
Year Published: 2017

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