Bootstrap estimation of long-run variance under strong dependence

Non-parametric Estimation under Strong Dependence.

We study non-parametric regression function estimation for models with strong dependence. Compared with short-range dependent models, long-range dependent models often result in slower convergence rates. We propose a simple differencing-sequence based non-parametric estimator that achieves the same convergence rate as if the data were independent. Simulation studies show that the proposed metho...

A theory of robust long-run variance estimation

Long-run variance estimation can typically be viewed as the problem of estimating the scale of a limiting continuous time Gaussian process on the unit interval. A natural benchmark model is given by a sample that consists of equally spaced observations of this limiting process. The paper analyzes the asymptotic robustness of long-run variance estimators to contaminations of this benchmark model...

M-estimation in nonparametric regression under strong dependence and infinite variance

A robust local linear regression smoothing estimator for a nonparametric regression model with heavy-tailed dependent errors is considered in this paper. Under certain regularity conditions, the weak consistency and asymptotic distribution of the proposed estimators are obtained. If the errors are short-range dependent, then the limiting distribution of the estimator is normal. If the data are ...

Long Run Variance Estimation Using Steep Origin Kernels without Truncation

Variance Estimation of Drug Abuse Episodes Using the Bootstrap

This paper examines the feasibility of using the bootstrap methodology to generate variance estimates of drug abuse episodes from a sample of hospital emergency rooms in the United States. The survey data comes from the Drug Abuse Warning Network (DAWN) which is a reporting system designed to monitor new and existing drugs of abuse based on estimates of total drug abuse episodes and the number ...