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.

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 [10.1107/S010876730501809X].

Outlier recognition in the crystal structure least-squares modelling by diagnostic techniques based on leverage analysis

MERLI, Marcello
2005-01-01

Abstract

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.
2005
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 [10.1107/S010876730501809X].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/4128
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