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We give a highly efficient "semi-agnostic" algorithm for learning univariate probability distributions that are well approximated by piecewise polynomial density functions. Let $p$ be an arbitrary dis...
Kernel estimation of a probability density function supported on the unit interval has proved difficult, because of the well known boundary bias issues a conventional kernel density estimator would ne...
We propose a method for nonparametric density estimation that exhibits robustness to contamination of the training sample. This method achieves robustness by combining a traditional kernel density est...
It is now practically the norm for data to be very high dimensional in areas such as genetics, machine vision, image analysis and many others. When analyzing such data, parametric models are often to...
Motivated by fluorescence lifetime measurements this paper considers the problem of nonparametric density estimation in the pile-up model. Adaptive nonparametric estimators are proposed for the pile-u...
we construct adaptive confidence bands that are honest for all densities in a “generic” subset of the union of t-H¨older balls, 0 < t  r, where r is a fixed but arbitrary integer. The exceptional (...
We study graph estimation and density estimation in high dimensions. To avoid the curse of dimensionality, we consider a family of density estimators based on tree structured undirected graphical mo...
A simple construction of polynomial estimators for densities and distributions on the unit interval is presented. For- Lipschitz densities the error for the mean square deviation is characterized. ...
Partial sums of the Fourier-Haar expansion in several variables are used to esstimate on cubes a probability density satisfying some Lipschitz conditions.
In [5] we have announced a h e a r spllne method for nonparametric density and distribution estimation on the real line. In this paper, asymptotic properties of a large family of such estimators a...
Empirical distributions in nance and economics might show heavy tails, volatility clustering, varying mean returns and multimodality as part of their features. However, most statistical models avail...
The use of variable kernel mass in density estimation
The purpose of this note is to provide an approximation for the generalized bootstrapped empirical process achieving the rate in Komlós et al. (1975). The proof is based onmuch the same arguments used...
The aim of this paper is to show the usefulness of Meyer wavelets for the classical problem of density estimation and for density deconvolution fromnoisy observations. By using suchwavelets, the comp...
We consider the statistical experiment given by a sample y(1), . . . , y(n) of a stationary Gaussian process with an unknown smooth spectral density f. Asymptotic equivalence, in the sense of Le Cam...

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