Estimation of Time Series Models via Robust Wavelet Variance

Estimation of Time Series Models via Robust Wavelet Variance

A robust approach to the estimation of time series models is proposed. Taking from a new estimation method called the Generalized Method of Wavelet Moments (GMWM) which is an indirect method based on the Wavelet Variance (WV), we replace the classical estimator of the WV with a recently proposed robust M-estimator to obtain a robust version of the GMWM. The simulation results show that the prop...

Robust Estimation of Wavelet Variance

The wavelet variance provides a scale-based decomposition of the process variance for a time series or a random field and has been used to analyze various multi-scale processes. Examples of such processes include atmospheric pressure, deviations in time as kept by atomic clocks, soil properties in agricultural plots, snow fields in the polar regions and brightness temperature maps of South Paci...

Variance Estimation in Time Series Regression Models

Wavelet Estimation of Copulas for Time Series

In this paper, we consider estimating copulas for time series, under mixing conditions, using wavelet expansions. The proposed estimators are based on estimators of densities and distribution functions. Some statistical properties of the estimators are derived and their performance assessed via simulations. Empirical applications to real data are also given. We are grateful to the Editor and tw...

Robust Nonparametric Estimation via Wavelet Median Regression

In this paper we develop a nonparametric regression method that is simultaneously adaptive over a wide range of function classes for the regression function and robust over a large collection of error distributions, including those that are heavy-tailed, and may not even possess variances or means. Our approach is to first use local medians to turn the problem of nonparametric regression with u...