Optimal designs for discrete choice models via graph Laplacians
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Publication:6408226
arXiv2208.08926MaRDI QIDQ6408226
Author name not available (Why is that?)
Publication date: 18 August 2022
Abstract: In discrete choice experiments, the information matrix depends on the model parameters. Therefore, -optimal designs are only locally optimal in the parameter space. This dependence renders the optimization problem very difficult, as standard theory encodes -optimality in systems of highly nonlinear equations and inequalities. In this work, we connect design theory for discrete choice experiments with Laplacian matrices of connected graphs. We rewrite the -optimality criterion in terms of Laplacians via Kirchhoff's matrix tree theorem, and show that its dual has a simple description via the Cayley--Menger determinant of the Farris transform of the Laplacian matrix. This results in a drastic reduction of complexity and allows us to implement a gradient descent algorithm to find locally -optimal designs. For the subclass of Bradley--Terry paired comparison models, we find a direct link to maximum likelihood estimation for Laplacian-constrained Gaussian graphical models. This implies that every locally -optimal design is a rational function in the parameter when the design is supported on a chordal graph. Finally, we study the performance of our algorithm and demonstrate its application on real and simulated data.
Has companion code repository: https://github.com/frank-unige/discrete_choice_designs_via_graph_laplacians
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