Brombin, Chiara and Pesarin, Fortunato and Fava, Giovanni (2005) Analisi multivariata per osservazioni appaiate con dati mancanti: un caso studio. [Working Paper] WORKING PAPER SERIES, 13/2005 . , PADOVA (Inedito)
Full text disponibile come:
All parametric approaches require that analysis should be done on complete data sets and so, in presence of missing data, parametric solutions are based either on the so-called deletion principle or imputation methods. But when we delete incomplete vectors we also remove all information they contain, which may be valuable and useful for analysis. And when we replace missing data by suitable functions of actually observed data, that is imputing method, we may introduce biased information which may negatively infuence the analysis. On the other hand, non-parametric solutions in a permutation framework consider data as they are, and units with missing data participate in the permutation mechanism as well as all other units, without deletion or imputing.
Statistiche Download - Aggiungi a RefWorks
Solo per lo Staff dell Archivio: Modifica questo record