An Overview of Lulu Smoothers With Application to Financial Data

Conradie, W. J.; Wet, Tertius de; Jankowitz, Maria Dorothea

doi:10.1080/10800379.2005.12106383

Cited by 3 publications

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Performance of nonlinear smoothers in signal recovery

Conradie

Wet

Jankowitz

2009

Appl Stoch Models Bus & Ind

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Time series data can be decomposed as signal plus noise. A good smoother should be able to recover a smooth signal reasonably well from time series data. The performance of two classes of nonlinear smoothers in signal recovery is discussed in this paper. The first class is the well-known class of median smoothers. The other one is a relatively new class of smoothers based on extreme-order statistics, called lower-upper-lower-upper smoothers. Sinusoidal signals of different frequencies with contaminated normal noise and impulsive noise added were simulated. Members of the two classes of nonlinear smoothers were applied to remove the 'non-Gaussian' and impulsive noise. To this output linear smoothing was applied to remove the remaining Gaussian noise. By means of a simulation study, the success of the two classes of smoothers was investigated using as measures of success the least-squares regression of the smoothed sequence on the signal and the integrated mean square error. PERFORMANCE OF NONLINEAR SMOOTHERS IN SIGNAL RECOVERY 429 SIGNAL RECOVERYSignal recovery is the process by which a signal is recovered from a set of noisy time series data, generated at time t as data t = signal t +noise t

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