Electronic Journal of Statistics

A recursive procedure for density estimation on the binary hypercube

Maxim Raginsky, Jorge G. Silva, Svetlana Lazebnik, and Rebecca Willett

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This paper describes a recursive estimation procedure for multivariate binary densities (probability distributions of vectors of Bernoulli random variables) 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 for moderate sample sizes, 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.

Article information

Electron. J. Statist. Volume 7 (2013), 820-858.

First available in Project Euclid: 25 March 2013

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G07: Density estimation
Secondary: 62G20: Asymptotic properties 62C20: Minimax procedures

Minimax estimation density estimation adaptive estimation binary hypercube Walsh basis sparsity


Raginsky, Maxim; Silva, Jorge G.; Lazebnik, Svetlana; Willett, Rebecca. A recursive procedure for density estimation on the binary hypercube. Electron. J. Statist. 7 (2013), 820--858. doi:10.1214/13-EJS787. https://projecteuclid.org/euclid.ejs/1364220672.

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