This chapter reviews recent advances in nonparametric and semiparametric estimation, with
an emphasis on applicability to empirical research and on resolving issues that arise in …

Motivated by the problem of setting prediction intervals in time series analysis, we suggest
two new methods for conditional distribution estimation. The first method is based on locally …

Deriving high quality daily rainfall estimates are required not only for successful hydrological
modelling but also for its application in ungauged basins. At present, there are two …

Estimation of finite population totals in the presence of auxiliary information is considered. A
class of estimators based on local polynomial regression is proposed. Like generalized …

A class of weighted bootstrap techniques, called biased bootstrap or b-bootstrap methods, is
introduced. It is motivated by the need to adjust empirical methods, such as the …

The standard approach to local linear regression involves fitting a straight line segment to a
curve in a symmetrical way, in that the segment is fitted directly above a small region whose …

As an alternative to traditional, parametric approaches, we suggest nonparametric methods
for analyzing spatial and temporal data on earthquake occurrences. Nonparametric …

This article introduces an interpolation-based method, called the reconstruction approach,
for nonparametric regression. Based on the fact that interpolation usually has negligible …

This paper considers using asymmetric kernels in local linear smoothing to estimate a
regression curve with bounded support. The asymmetric kernels are either beta kernels if …

This paper provides a simulation study of several popular bandwidth selectors for local
linear regression. The study also includes two new selectors which couple the non …