We define a nonparametric prewhitening method for estimating conditional quantiles based on local linear quantile regression. We characterize the bias, variance and asymptotic normality of the proposed estimator. Under weak conditions our estimator can achieve bias reduction and have the same variance as the local linear quantile estimators. A small set of Monte Carlo simulations is carried out...

In this note, we prove that the projective normality of (P(V )/G,L), the celebrated theorem of Erdös-Ginzburg-Ziv and normality of an affine semigroup are all equivalent, where V is a finite dimensional representation of a finite cyclic group G over C and L is the descent of the line bundle O(1)⊗|G|.

In this article we study nonparametric estimation of regression function by using the weighted Nadaraya-Watson approach. We establish the asymptotic normality and weak consistency of the resulting estimator for-mixing time series at both boundary and interior points, and we show that the estimator preserves the bias, variance, and more importantly, automatic good boundary behavior properties of...

In this article, we investigate a robust version of local linear regression smoothers for stationary and censored stochastic processes by using M-type local polynomial techniques and transformations. Under some regularity conditions, we establish the weak and strong consistency as well as the asymptotic normality of proposed estimators. We propose an easily implemented bandwidth selection crite...

We consider a local least-squares criterion for aligning multiple time series fragments differing by locations and show the consistency of the time-lag estimator and the asymptotic normality of the location estimator. We apply the criterion to the problem of aligning 50 glacial varve fragments and construct a 3000-year surrogate for global temperature. Some key words: Aligning time series; Asym...

This paper considers a modified empirical likelihood and uses it to estimate models with imperfect moment restrictions. Consistency, local normality and asymptotic optimality of the new estimator only require the local identifiability and the linear-quadratic representation. In imperfect situations, classical sandwich algorithms give bias estimations while the new estimator preserves its asympt...

This paper considers local M-estimation of the unknown drift and diffusion functions of integrated diffusion processes. We show that under appropriate conditions, the proposed estimators for drift and diffusion functions in the integrated process are consistent, and the conditions that ensure the asymptotic normality of these local M-estimators are also stated. The simulation studies show that ...

Locally asymptotically most stringent tests for autoregressive against diagonal bilinear time series models are derived. A (restricted) local asymptotic normality property is therefore established for bilinear processes in the vicinity of linear autoregressive ones. The behaviour of the bispectrum under local alternatives of bilinear dependence shows the danger of misinterpreting skewness or ku...