TY - JOUR

T1 - A wavelet lifting approach to long-memory estimation

AU - Knight, Marina

AU - Nason, Guy P.

AU - Nunes, Matthew Alan

N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-016-9698-2

PY - 2017/11

Y1 - 2017/11

N2 - Reliable estimation of long-range dependence parameters is vital in time series. For example, in environmental and climate science such estimation is often key to understanding climate dynamics, variability and often prediction. The challenge of data collection in such disciplines means that, in practice, the sampling pattern is either irregular or blighted by missing observations. Unfortunately, virtually all existing Hurst parameter estimation methods assume regularly sampled time series and require modification to cope with irregularity or missing data. However, such interventions come at the price of inducing higher estimator bias and variation, often worryingly ignored. This article proposes a new Hurst exponent estimation method which naturally copes with data sampling irregularity. The new method is based on a multiscale lifting transform exploiting its ability to produce wavelet-like coefficients on irregular data and, simultaneously, to effect a necessary powerful decorrelation of those coefficients. Simulations showthat our method is accurate and effective, performing well against competitors even in regular data settings. Armed with this evidence our method sheds new light on long-memory intensity results in environmental and climate science applications, sometimes suggesting that differentscientific conclusions may need to be drawn.

AB - Reliable estimation of long-range dependence parameters is vital in time series. For example, in environmental and climate science such estimation is often key to understanding climate dynamics, variability and often prediction. The challenge of data collection in such disciplines means that, in practice, the sampling pattern is either irregular or blighted by missing observations. Unfortunately, virtually all existing Hurst parameter estimation methods assume regularly sampled time series and require modification to cope with irregularity or missing data. However, such interventions come at the price of inducing higher estimator bias and variation, often worryingly ignored. This article proposes a new Hurst exponent estimation method which naturally copes with data sampling irregularity. The new method is based on a multiscale lifting transform exploiting its ability to produce wavelet-like coefficients on irregular data and, simultaneously, to effect a necessary powerful decorrelation of those coefficients. Simulations showthat our method is accurate and effective, performing well against competitors even in regular data settings. Armed with this evidence our method sheds new light on long-memory intensity results in environmental and climate science applications, sometimes suggesting that differentscientific conclusions may need to be drawn.

KW - Hurst exponent

KW - Irregular sampling

KW - Long-range dependence

KW - Wavelets

U2 - 10.1007/s11222-016-9698-2

DO - 10.1007/s11222-016-9698-2

M3 - Journal article

VL - 27

SP - 1453

EP - 1471

JO - Statistics and Computing

JF - Statistics and Computing

SN - 0960-3174

IS - 6

ER -