Dirty data

Figure 9. Comparison between performance of error-weighted least-squares isochron (dashed lines) with a robust isochron (solid line) for a dirty data-set at secular equilibrium). Both age and errors are greatly affected by inclusion of the two outliers (stippled error ellipses), whereas the robust isochron (Theil 1950 Vugrinovich 1981) is unchanged. Errors are 95% confidence.

Further development of robust methods tailored for isochron data can be anticipated, with the ultimate goal being a method that yields the same errors as EWLS for well-behaved data (i.e., strictly Gaussian, with accurate estimates of data-point errors), and only modest loss of precision for increasingly dirty data (R. Powell, written commun. 2002). 

The SAP will have detailed the precise methods of analysis and presentation and should ideally be finalised well before database lock. This enables work to begin in good time on the programming of the analyses. These programs will be tested on dirty data from the trial, so that they can be pretty much finalised before the trial ends, enabling, at least in theory, rapid turnaround of the key analyses. 

The wa in equation (6) are the PLS loading weights. They are explained in the theory in references 53 - 62. Equation (7) shows how X is decomposed bilinearly (as in principal component analysis) with its own residual Epls A. T is the matrix with the score vectors as columns, P is the matrix having the PLS loadings as columns. Also the vectors of P and wa can be used to construct scatter plots. These can reveal the data structure of the variable space and relations between variables or groups of variables. Since PLS mainly looks for sources of variance, it is a very good dirty data technique. Random noise will not be decomposed into scores and loadings, and will be stored in the residual matrices (E and F), which contain only non-explained variance . 

Robust statistics. Statistics which are less vulnerable to the presence of outliers and dirty data but which are generally slightly less efficient than the standard alternatives where the data are well behaved. The median, for example, is a robust statistic, whereas the mean is not. The mountain-bikes of the statistical world as opposed to its road-racers. A means to drink salt-water as if it were sweet. 

In Sect. 5.1 the process that converts the recorded interferograms to the dirty data cube is described through a master simulation, and possibilities for the correction of the instrumental artifacts are shown. In Sect. 5.3 the validation of FllnS is presented following the description of the FIRI testbed shown in Chap. 3. In this case, a one dimensional analysis is given. 

Interferometric Image Synthesis The Dirty Data Cube... 

Fig. 5.6 Spatial layers of the dirty data cube for the Master simulation, corresponding to the minimum wavenumber (25cm , left), central wavenumber (118cm , centre) and maximum wavenumber (212cm , right) top). Telescope beam for each of these wavenumbers bottom)...

By integrating the dirty data cube over wavenumber one can extract the spatial features of the sky, because the dirty beam ripples at different frequencies or wavenumbers cancel and hence the ratio between the power of the central lobe and the secondary lobe increases. However, this is only useful if the sources on the sky have similar size and power. 

When 5 blind deconvolution iterations are applied to the dirty data cube, the beam size does not correspond to the one expected from theory anymore, from which one can infer that the recovered datacube is unrealistic. 

The input to AIPS is the dirty image (in this case the dirty data cube) and the dirty beam. After applying the CLEAN algorithm for each spectral channel, the CLEAN data cube is obtained. 

Fig. 6.4 Spatial layers of the reconstructed dirty data cube corresponding to six different wavenumbers (shown on top of each image) in the band of operation of the system for an ideal instrument simulation...

Dirty data Product proiiferation Rising commodity prices Taient shortage Changing market preferences Cost of iT Compiiance and iegisiation Competition Other... 

Supply chain talent, as shown in Figure 6.9, ranks fourth as a gap in priority for supply chain executives today, ft follows dirty data, product proliferation, and commodity price escalation. As the first generation of supply chain pioneers continue to retire through 2012, it will become a more pressing issue. To solve it, a new industry will evolve to support the development of company-specific training. To help companies understand the complexity of supply chain systems, new forms of experiential training programs will evolve. 

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