Wavelet analysis is a new mathematical tool developed as a unified field of science over the last decade. As spatially adaptive analytic tools, wavelets are useful for capturing serial correlation where the spectrum has peaks or kinks, as can arise from persistent/strong dependence, seasonality...

This paper proposes an adjusted-range based self-normalization method to construct confidence intervals for censored dependent data, which helps to circumvent the long-run variance estimation and tuning parameter selection problems. Simulation studies confirm the validity of this new approach

Modelling and forecasting symbolic data, especially interval-valued time series (ITS) data, has received considerable attention in statistics and related fields. The core of available methods on ITS analysis is based on various applications of conventional linear modelling. However, few works...

Modelling and forecasting symbolic data, especially interval-valued time series (ITS) data, has received considerable attention in statistics and related fields. The core of available methods on ITS analysis is based on various applications of conventional linear modelling. However, few works...