Unlike symmetric kernels, so far exploring asymptotics on asymmetric kernels has relied on
diversified approaches. This paper proposes a family of the generalised gamma (GG) …

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 …

Kernels are typically justified as a smoothing device in nonparametric analysis. We provide
alternative interpretations, which could lead to the use of asymmetric kernels. We thus derive …

Testing symmetry or quantile assumptions on the innovations distribution can be of
invaluable help to improve or simplify the statistical procedures designed for GARCH-type …

Discontinuity in density functions is of economic importance and interest. For instance, in
studies on regression discontinuity designs, discontinuity in the density of a running variable …

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 …

This paper improves a kernel-smoothed test of symmetry through combining it with a new
class of asymmetric kernels called the generalized gamma kernels. It is demonstrated that …

We propose new data driven score rank tests for univariate symmetry about an unknown
center. We construct test statistics, state assumptions and define estimators of nuisance …

There are only a few accomplishments on applying asymmetric kernels to specification
testing. This chapter deals with three model specification tests. Each test has an application …

abstract The skewing method, which has been originally proposed as a bias correction
device for local linear regression estimation using standard symmetric kernels, is extended …