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Efficient Density Estimation via Piecewise Polynomial Approximation
Efficient Density Estimation Piecewise Polynomial Approximation
2013/6/14
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...
Probit transformation for kernel density estimation on the unit interval
transformation kernel density estimator boundary bias local likelihood density estimation local log-polynomial density estimation
2013/4/27
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...
A comparative study of new cross-validated bandwidth selectors for kernel density estimation
kernel density estimation data-adaptive bandwidth selection indirect cross-validation do-validation.
2012/11/22
Recent contributions to kernel smoothing show that the performance of cross-validated bandwidth selectors improve significantly from indirectness. Indirect crossvalidation first estimates the classica...
Exploring wind direction and SO2 concentration by circular-linear density estimation
Circular distributions Circular kernel estimation Circular{linear data Copula.
2012/9/17
The study of environmental problems usually requires the description of variables with di er-ent nature and the assessment of relations between them. In this work, an algorithm for exible estimation o...
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...
k-Nearest neighbor density estimation on Riemannian Manifolds
Asymptotic results Density estimation Meteorological applications
2011/7/6
In this paper, we consider a k-nearest neighbor kernel type estimator when the random variables belong in a Riemannian manifolds.
Density Estimation and Classification via Bayesian Nonparametric Learning of Affine Subspaces
Dimension reduction Classi er Variable selection Nonparametric Bayes
2011/6/20
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...
Adaptive Density Estimation in the Pile-up Model Involving Measurement Errors
Adaptive nonparametric estimation Deconvolution Fluorescence lifetimes
2010/11/8
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...
Asymptotic minimax risk of predictive density estimation for non-parametric regression
asymptotic minimax risk convergence rate non-parametric regression
2010/10/19
We consider the problem of estimating the predictive density of future observations from a non-parametric regression model. The density estimators are evaluated under Kullback--Leibler divergence and ...
Confidence bands in density estimation
Adaptive estimation limit theorem density estimation extremes Gaussian processes wavelet estimators kernel estimators
2010/3/11
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 (...
Tree Density Estimation
kernel density estimation tree structured Markov network high dimensional inference risk consistency structure selection consistency
2010/3/9
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.
...
Haar system and nonparametric density estimation in several variables
Haar system nonparametric density estimation several variables
2009/9/23
Partial sums of the Fourier-Haar expansion in several
variables are used to esstimate on cubes a probability density
satisfying some Lipschitz conditions.
Asymptotic nonparametric spline density estimation
Asymptotic nonparametric spline density estimation
2009/9/23
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...
Bayesian Dynamic Density Estimation
Dependent Dirichlet process Nonparametric Bayes Random probability measure Travel Costs Insurance Claim Distributions
2009/9/22
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...