Local polynomial regression is widely used for nonparametric regression. However, the efficiency of least squares (LS) based methods is adversely affected by outlying observations and heavy tailed distributions. On the other hand, the least absolute deviation (LAD) estimator is more robust, but...

Data collecting institutions use a large range of masking procedures in order to protect data against disclosure. Generally, a masking procedure can be regarded as a kind of data filter that transforms the true data generating process. Such a transformation severely affects the quality of the...

<Para ID="Par1">This paper is concerned about robust comparison of two regression curves. Most of the procedures in the literature are least-squares-based methods with local polynomial approximation to nonparametric regression. However, the efficiency of these methods is adversely affected by outlying...</para>

The main objective of the paper is to analyse the local determinants of innovation in the Luxembourg metropolitan region. We are particularly interested in the impact of the local milieu and characteristics of firms. Our paper addresses two specific research questions. Firstly, we examine the...

In this paper, we consider three major types of nonparametric regression tests that are based on kernel and local polynomial smoothing techniques. Their asymptotic power comparisons are established systematically under the fixed and contiguous alternatives, and are also illustrated through...

This article considers empirical likelihood in the context of efficient semiparametric estimators of average treatment effects. It shows that the empirical likelihood ratio converges to a nonstandard distribution, and proposes a corrected test statistic that is asymptotically chi-squared. A...

This paper is concerned with composite quantile regression for single-index models. Under mild conditions, we show that the linear composite quantile regression offers a consistent estimate of the index parameter vector. With a root-n consistent estimate of the index vector, the unknown link...

We deal with nonparametric estimation in a nonlinear cointegration model whose regressor and error term can be contemporaneously correlated. The asymptotic properties of the Nadaraya–Watson estimator are already examined in the literature. In this paper, we consider nonparametric least...

Motivated by the first-differencing method for linear panel data models, we propose a class of iterative local polynomial estimators for nonparametric dynamic panel data models with or without exogenous regressors. The estimators utilize the additive structure of the first-differenced...