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We propose a Cholesky factor parameterization of correlation matrices that facilitates a priori restrictions on the correlation matrix. It is a smooth and differentiable transform that allows additional boundary constraints on the correlation values. Our particular motivation is random sampling under positivity constraints on the space of correlation matrices using MCMC methods.
Sean Pinkney (2024) studied this question.
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