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2008

Near-minimax recursive density estimation on the binary hypercube

11 years 1 months ago
Near-minimax recursive density estimation on the binary hypercube
This paper describes a recursive estimation procedure for multivariate binary densities using orthogonal expansions. For d covariates, there are 2d 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.
Maxim Raginsky, Svetlana Lazebnik, Rebecca Willett
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Maxim Raginsky, Svetlana Lazebnik, Rebecca Willett, Jorge Silva
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