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Near-minimax recursive density estimation on the binary hypercube
Maxim Raginsky · Svetlana Lazebnik · Rebecca Willett · Jorge G Silva

Tue Dec 09 03:24 PM -- 03:25 PM (PST) @ None
This paper describes a recursive estimation procedure for multivariate binary densities using orthogonal expansions. For $d$ covariates, there are $2^d$ basis coefficients to estimate, which renders conventional approaches computationally prohibitive when $d$ is large. However, for a wide class of densities that satisfy a certain sparsity condition, our estimator runs in probabilistic polynomial time and adapts to the unknown sparsity of the underlying density in two key ways: (1) it attains near-minimax mean-squared error, and (2) the computational complexity is lower for sparser densities. Our method also allows for flexible control of the trade-off between mean-squared error and computational complexity.

Author Information

Maxim Raginsky (University of Illinois at Urbana-Champaign)
Svetlana Lazebnik (UIUC)
Rebecca Willett (Duke University)
Jorge G Silva (Duke University)

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