MULTISCALE BERNSTEIN POLYNOMIALS FOR DENSITIES

作者：C****e Antonio^*; Dunson David B

来源：Statistica Sinica, 2016, 26(3): 1175-1195.

DOI：10.5705/ss.202015.0163

• Density estimation
• multiresolution
• multiscale clustering
• multiscale testing
• nonparametric Bayes
• Polya tree
• stick-breaking
• wavelets

Our focus is on constructing a multiscale nonparametric prior for densities. The Bayes density estimation literature is dominated by single scale methods, with the exception of Polya trees, which favor overly-spiky densities even when the truth is smooth. We propose a multiscale Bernstein polynomial family of priors, which produce smooth realizations that do not rely on hard partitioning of the support. At each level in an infinitely-deep binary tree, we place a beta dictionary density; within a scale the densities are equivalent to Bernstein polynomials. Using a stick-breaking characterization, stochastically decreasing weights are allocated to the finer scale dictionary elements. A slice sampler is used for posterior computation, and properties are described. The method characterizes densities with locally-varying smoothness, and can produce a sequence of coarse to fine density estimates. An extension for Bayesian testing of group differences is introduced and applied to DNA methylation array data.

• 出版日期2016-7