Add to ORKGIn this paper, we consider kernel type estimator with variable bandwidth when the random variables belong in a Riemannian manifolds. We study asymptotic properties such as the consistency and the asymptotic distribution. A simulation study is also considered to evaluate the performance of the proposal. Finally, to illustrate the potential applications of the proposed estimator, we analyse two real examples where two different manifolds are considered.Peer Reviewe
We consider practical density estimation from large data sets sampled on manifolds embedded in Eucli...
Asymptotic properties of a kernel density estimator using a random bandwidth are difficult to establ...
Results on nonparametric kernel estimators of density differ according to the assumed degree of dens...
In this paper, we consider kernel type estimator with variable bandwidth when the random variables b...
In this paper, we consider kernel type estimator with variable bandwidth when the random variables b...
In this paper, we consider kernel type estimator with variable bandwidth when the random variables b...
In this paper, we consider kernel type estimator with variable bandwidth when the random variables b...
The paper concerns the strong uniform consistency and the asymptotic distribution of the kernel dens...
In this paper, we consider a k-nearest neighbor kernel type estimator when the random variables belo...
The estimation of the underlying probability density of n i.i.d. random objects on a compact Riemann...
Abstract. We propose a new type of non parametric density estimators fitted to nonnegative random va...
We propose a new type of non parametric density estimators fitted to nonnegative random variables. T...
Abstract Multivariate versions of variable bandwidth kernel density estimators can lead to improveme...
Nonparametric density estimation on Riemannian surfaces is performed by inducing a prior through a l...
Nonparametric density estimation on Riemannian surfaces is performed by inducing a prior through a l...
We consider practical density estimation from large data sets sampled on manifolds embedded in Eucli...
Asymptotic properties of a kernel density estimator using a random bandwidth are difficult to establ...
Results on nonparametric kernel estimators of density differ according to the assumed degree of dens...
In this paper, we consider kernel type estimator with variable bandwidth when the random variables b...
In this paper, we consider kernel type estimator with variable bandwidth when the random variables b...
In this paper, we consider kernel type estimator with variable bandwidth when the random variables b...
In this paper, we consider kernel type estimator with variable bandwidth when the random variables b...
The paper concerns the strong uniform consistency and the asymptotic distribution of the kernel dens...
In this paper, we consider a k-nearest neighbor kernel type estimator when the random variables belo...
The estimation of the underlying probability density of n i.i.d. random objects on a compact Riemann...
Abstract. We propose a new type of non parametric density estimators fitted to nonnegative random va...
We propose a new type of non parametric density estimators fitted to nonnegative random variables. T...
Abstract Multivariate versions of variable bandwidth kernel density estimators can lead to improveme...
Nonparametric density estimation on Riemannian surfaces is performed by inducing a prior through a l...
Nonparametric density estimation on Riemannian surfaces is performed by inducing a prior through a l...
We consider practical density estimation from large data sets sampled on manifolds embedded in Eucli...
Asymptotic properties of a kernel density estimator using a random bandwidth are difficult to establ...
Results on nonparametric kernel estimators of density differ according to the assumed degree of dens...