Resampling for stationary sequences has been well studied in the last couple of decades. In the paper at hand, we focus on nonstationary time series data where the nonstationarity is due to a slowly-changing deterministic trend. We show that the local block bootstrap methodology is appropriate...

This paper considers the problem of parameter estimation in a general class of semiparametric models when observations are subject to missingness at random. The semiparametric models allow for estimating functions that are non-smooth with respect to the parameter. We propose a nonparametric...

The intention is to provide a Bayesian formulation of regularized local linear regression, combined with techniques for optimal bandwidth selection. This approach arises from the idea that only those covariates that are found to be relevant for the regression function should be considered by the...

Many univariate robust estimators are based on quantiles. As already theoretically pointed out by Fernholz (in J. Stat. Plan. Inference 57(1), 29–38, <CitationRef CitationID="CR7">1997</CitationRef>), smoothing the empirical distribution function with an appropriate kernel and bandwidth can reduce the variance and mean squared error...</citationref>

The productivity of public capital has been very popular research topic for US and other OECD countries, while studies using data from transitional countries are almost non-existent. In this paper, we analyze the productivity of public and private capital in Russia with parametric and...

Weighting is a largely used concept in many fields of statistics and has frequently cause controversies on its justification and profit. In this paper, we analyze a weighted version of the well-known local polynomial regression estimators, derive their asymptotic bias and variance, and find that...