Description Usage Arguments Value Author(s) References See Also Examples

Performs statistical wavelet deconvolution using Meyer wavelet.

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`yobs` |
Sample of |

`g` |
Sample of |

`MC` |
Option to only return the (fast) translation-invariant WaveD estimate (MC=TRUE) as opposed to the full WaveD output (MC=FALSE, the default), as described below. MC=TRUE recommended for Monte Carlo simulation. |

`SOFT` |
if SOFT=TRUE, uses the soft thresholding policy as opposed to the hard (SOFT=FALSE, the default). |

`F` |
Finest resolution level; the default is the data-driven choice j1 (see Value below). |

`L` |
Lowest resolution level; the default is 3. |

`deg` |
The degree of the Meyer wavelet, either 1, 2, or 3 (the default). |

`eta` |
Tuning parameter of the maxiset threshold; default is |

`thr` |
A vector of length |

`label` |
Auxiliary plotting parameter; do not change this. |

In the case that MC=TRUE, WaveD returns a vector consisting of the translation-invariant WaveD estimate. In the case that MC=FALSE (the default), WaveD returns a list with components

`waved` |
translation invariant WaveD transform; in the case MC=TRUE this is all that is returned. |

`ordinary` |
ordinary WaveD transform |

`FWaveD` |
Forward WaveD Transform; see |

`w` |
alternate name for FWaveD |

`w.thr` |
thresholded version of w |

`IWaveD` |
Inverse WaveD Transform |

`iw` |
alternate name for IWaveD |

`s` |
estimate of the noise standard deviation |

`j1` |
estimate of optimal resolution level (for maxiset threshold). |

`F` |
Fine resolution level used (may be different to j1). |

`M` |
estimate of optimal Fourier frequency (for maxiset threshold). |

`thr` |
vector of thresholds used (default is maxiset threshold). |

`percent` |
percentage of thresholding per resolution level |

`noise` |
noise proxy, wavelet coefficients of the raw data at the largest resolution level, used for estimating noise features. |

`ps` |
P-value of the Shapiro-Wilk test for normality applied to the noise proxy. |

`residuals` |
wavelet coefficients that have been removed before fine level F. |

Marc Raimondo and Michael Stewart

Cavalier, L. and Raimondo, M. (2007), ‘Wavelet deconvolution with noisy eigen-values’, * IEEE Trans. Signal
Process*, Vol. 55(6), In the press.

Donoho, D. and Raimondo, M. (2004),
‘Translation invariant deconvolution in a periodic setting’, * The
International Journal of Wavelets, Multiresolution and Information
Processing* **14**(1),415–423.

Johnstone, I., Kerkyacharian, G., Picard, D. and Raimondo, M. (2004),
'Wavelet deconvolution in a periodic
setting', * Journal of the Royal Statistical Society, Series B* **
66**(3),547–573. with discussion pp.627–652.

Raimondo, M. and Stewart, M. (2007), ‘The WaveD Transform in R’, Journal of Statistical Software.

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