Hurst Exponent Estimation for Irregularly Sampled Processes Using Wavelet Lifting

• Activity Number: 575
• Type: Contributed
• Date/Time: Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Matthew Nunes*+ and Marina Knight and Guy Nason
• Companies: Lancaster University and University of York and University of Bristol
• Keywords: time series ; long memory ; Hurst exponent ; irregular observations ; missingness ; wavelet lifting

Abstract

Reliable estimation of long-range dependence (LRD) parameters, such as the Hurst exponent, is a well-studied problem in the statistical literature. However, many time series observed in practice present missingness or are naturally irregularly-sampled. In these settings, current literature is sparse; most approaches require heavy modifications to deal with the irregular observations.

In this talk we present a technique for estimating the Hurst exponent of an long memory time series. The method is based on a flexible wavelet transform built via the lifting scheme, and is naturally suitable for series exhibiting time domain irregularity. We illustrate the technique through a time series application in climatology.