The identification of the actual outliers in a least-squares crystal-structure model refinement and their subsequent elimination from the data set is a non-trivial task that has to be carried out carefully when a high level of accuracy of the estimates is required. One of the most suitable tools for detecting the influence of each data entry on the regression is the identification of ‘leverage points’. On the other hand, the recognition of the actual statistical outliers is effectively possible by using some diagnostics as a function of the leverage, such as Cook’s distance, DFFITS and FVARATIO. The evaluation of these estimators makes it possible to achieve a reliable identification of the outliers and the elimination of those that impair the least-squares fit. In this paper, a procedure for filtering data points based on this kind of analysis for crystallographic X-ray data is presented and discussed.
|Data di pubblicazione:||2005|
|Titolo:||Outlier recognition in the crystal structure least-squares modelling by diagnostic techniques based on leverage analysis|
|Tipologia:||Articolo su rivista|
|Citazione:||MERLI M (2005). Outlier recognition in the crystal structure least-squares modelling by diagnostic techniques based on leverage analysis. ACTA CRYSTALLOGRAPHICA. SECTION A, FOUNDATIONS OF CRYSTALLOGRAPHY, A61, 471-477.|
|Digital Object Identifier (DOI):||10.1107/S010876730501809X|
|Appare nelle tipologie:||01 - Articolo su rivista|