Title: A new method of joint nonparametric estimation of probability density and its support,
Abstract: In this paper we propose a new method of joint nonparametric estimation of
probability density and its support. As is well known, nonparametric kernel
density estimator has "boundary bias problem" when the support of the
population density is not the whole real line. To avoid the unknown boundary
effects, our estimator detects the boundary, and eliminates the boundary-bias
of the estimator simultaneously. Moreover, we refer an extension to a simple
multivariate case, and propose an improved estimator free from the unknown
boundary bias.