Pr{é}diction optimale pour un mod{è}le ordinal {à} covariables fonctionnelles
This work addresses a domain-specific problem in manufacturing or product development, likely incremental as it adapts existing ordinal models to functional covariates.
The paper tackles the problem of predicting ordinal outcomes with functional covariates by introducing optimal predictions using loss functions and reformulating the model to handle scalar covariates. The methods are applied to a dataset from EssilorLuxottica for controlling the shade of connected glasses, but no concrete numerical results are provided.
We present a prediction framework for ordinal models: we introduce optimal predictions using loss functions and give the explicit form of the Least-Absolute-Deviation prediction for these models. Then, we reformulate an ordinal model with functional covariates to a classic ordinal model with multiple scalar covariates. We illustrate all the proposed methods and try to apply these to a dataset collected by EssilorLuxottica for the development of a control algorithm for the shade of connected glasses.