Applications of Bernstein polynomials in Statistics
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If you have a question about this talk, please contact Richard Nickl.
Bernstein polynomials are studied in analysis as a probabilistic
approximation scheme, and they are well known to provide a constructive
proof of Weirstrass theorem. In fact, their simple probabilistic nature make
them also attractive in many statistical problems.
In this talk I will give an overview of applications of Bernstein
polynomials in statistics, in particular in Bayesian nonparametric
inference.
I will briefly review their use to construct a nonparametric prior, which
provides a smoothing of a Dirichlet process, and allows to fairly easily
incorporate prior information. I will discuss theoretical properties and
show some applications, in particular to shrinkage estimation, comparing
with the clustering properties of the Dirichlet process. Bernstein
polynomials also arise in quantile estimation from a Dirichlet process. I
will finally discuss extensions to data on a general subset of R and to
multivariate data.
This talk is part of the Statistics series.
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