Abstract
In this study, we consider parametric binary choice models from the perspective of information geometry. The set of models is a dually flat manifold with dual connections, which are naturally derived from the Fisher information metric. Under the dual connections, the canonical divergence and the Kullback–Leibler (KL) divergence of the binary choice model coincide if and only if the model is a logit. The results are applied to a logit estimation with linear constraints.
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Tanaka, H. (2023). Geometry of Parametric Binary Choice Models. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2023. Lecture Notes in Computer Science, vol 14071. Springer, Cham. https://doi.org/10.1007/978-3-031-38271-0_16
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DOI: https://doi.org/10.1007/978-3-031-38271-0_16
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