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Adaptation in log-concave density estimation

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STS - Statistical scalability

The log-concave maximum likelihood estimator of a density on R^d on a sample of size n is known to attain the minimax optimal rate of convergence up to a log factor when d = 2 and d = 3. In this talk, I will review the univariate adaptation result, and will present new results on adaptation properties in the multivariate setting. This is based on joint work with Oliver Feng, Aditya Guntuboyina and Richard Samworth



This talk is part of the Isaac Newton Institute Seminar Series series.

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