We study theoretically, for the first time, the Dirichlet kernel estimator introduced by Aitchison
and Lauder (1985) for the estimation of multivariate densities supported on the d …

The varying kernel density estimates are particularly designed for positive random variables.
Unlike the commonly used symmetric kernel density estimates, the varying kernel density …

We consider density estimation for nonnegative data using generalised gamma density.
What is being emphasised here is that a negative exponent is allowed. We show that, for …

Abstract The Birnbaum–Saunders distribution has been generalized in various ways, for
parametric or nonparametric statistical inference. In this paper, as a remedy for the boundary …

Let {X i, i≥ 1} be a sequence of independent and identically distributed random variables
with distribution function F and probability density function f. We propose new type of kernel …

In this paper, we consider a generalised kernel smoothing estimator of the regression
function with non-negative support, using gamma probability densities as kernels, which are …

A multivariate Birnbaum–Saunders distribution is introduced to consider a new
nonparametric multivariate density estimation for nonnegative data. By construction, the …

In this paper, we introduce Dirichlet kernels for the estimation of multivariate densities
supported on the d-dimensional simplex. These kernels generalize the beta kernels from …

Nous introduisons une nouvelle approche non-paramétrique, par noyaux associés discrets,
pour les données de dénombrement. Pour cela, nous définissons la notion de noyaux …

Here, we apply the smoothing technique proposed by Chaubey et al. for the empirical
survival function studied in Bagai and Prakasa Rao for a sequence of stationary non …