AbstractWe consider bivariate logspline density estimation for tomography data. In the usual logspline density estimation for bivariate data, the logarithm of the unknown density function is estimated by tensor product splines, the unknown parameters of which are given by maximum likelihood. In this paper we use tensor product B-splines and the projection-slice theorem to construct the logspline density estimators for tomography data. Rates of convergence are established for log-density functions assumed to belong to a Besov space
In this work, three extensions of univariate nonparametric probability density estimators into two d...
Given a random sample from a continuous and positive density f , the logistic transformation is app...
We study the adaptation properties of the multivariate log-concave maximum likelihood estimator over...
We consider bivariate logspline density estimation for tomography data. In the usual logspline densi...
A Logspline method of estimating an unknown density function f based on sample data is studied. Our ...
We study the rate of convergence of posterior distributions in density estimation problems for log-d...
We study the rate of convergence of posterior distributions in density estimation problems for log-d...
The estimation of a log-concave density on Rd represents a central problem in the area of nonparamet...
Most recent maximum likelihood approaches to independent component analysis (ICA) are based on nonpa...
Logspline density estimation is developed for data that may be right censored, left censored or inte...
For density estimation and nonparametric regression, block thresholding is very adaptive and efficie...
The log-concave maximum likelihood estimator of a density on the real line based on a sample of size...
AbstractThere have important applications of density kernel estimation in statistics. In certain con...
Tech ReportThe nonparametric density estimation method proposed in this paper is computationally fas...
We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric ...
In this work, three extensions of univariate nonparametric probability density estimators into two d...
Given a random sample from a continuous and positive density f , the logistic transformation is app...
We study the adaptation properties of the multivariate log-concave maximum likelihood estimator over...
We consider bivariate logspline density estimation for tomography data. In the usual logspline densi...
A Logspline method of estimating an unknown density function f based on sample data is studied. Our ...
We study the rate of convergence of posterior distributions in density estimation problems for log-d...
We study the rate of convergence of posterior distributions in density estimation problems for log-d...
The estimation of a log-concave density on Rd represents a central problem in the area of nonparamet...
Most recent maximum likelihood approaches to independent component analysis (ICA) are based on nonpa...
Logspline density estimation is developed for data that may be right censored, left censored or inte...
For density estimation and nonparametric regression, block thresholding is very adaptive and efficie...
The log-concave maximum likelihood estimator of a density on the real line based on a sample of size...
AbstractThere have important applications of density kernel estimation in statistics. In certain con...
Tech ReportThe nonparametric density estimation method proposed in this paper is computationally fas...
We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric ...
In this work, three extensions of univariate nonparametric probability density estimators into two d...
Given a random sample from a continuous and positive density f , the logistic transformation is app...
We study the adaptation properties of the multivariate log-concave maximum likelihood estimator over...