Uniform asymptotic properties of a nonparametric regression estimator of conditional tails

Uniform asymptotic properties of a nonparametric regression estimator of conditional tails

We consider a nonparametric regression estimator of conditional tails introduced by Goegebeur, Y., Guillou, A., Schorgen, G. (2012). Nonparametric regression estimation of conditional tails the random covariate case. It is shown that this estimator is uniformly strongly consistent on compact sets and its rate of convergence is given. AMS Subject Classi cations: 62G05, 62G20, 62G32.

A Smooth Nonparametric Conditional Quantile Frontier Estimator

Traditional estimators for nonparametric frontier models (DEA, FDH) are very sensitive to extreme values/outliers. Recently, Aragon, Daouia, and Thomas-Agnan (2005) proposed a nonparametric α-frontier model and estimator based on a suitably defined conditional quantile which is more robust to extreme values/outliers. Their estimator is based on a nonsmooth empirical conditional distribution. In...

Asymptotic Normality of a Nonparametric Conditional Quantile Estimator for Random Fields

A Nonparametric Regression Spectrum : Estimation, Asymptotic Properties and Data Analysis

Classical spectral analysis in statistics considers decomposition of stationary time series into sinusoidal components. The autocovariance and the spectrum are fundamental elements for analyzing a given time series both in time and frequency domain. However, in practice one frequently observes nonstationary time series. In order to apply spectral analysis to these processes, an extension of the...

Robust nonparametric kernel regression estimator

In robust nonparametric kernel regression context,weprescribemethod to select trimming parameter and bandwidth. Through solving estimating equations, we control outlier effect through combining weighting and trimming. We show asymptotic consistency, establish bias, variance properties and derive asymptotics. © 2016 Elsevier B.V. All rights reserved.