Two methods based on the Forward Imputation approach are implemented for the imputation of quantitative missing data. One method alternates Nearest Neighbour Imputation and Principal Component Analysis (function 'ForImp.PCA'), the other uses Nearest Neighbour Imputation with the Mahalanobis distance (function 'ForImp.Mahala').

GenForImp: The Forward Imputation : A Sequential Distance-Based Approach for Imputing Missing Data [Software] / N. Solaro, A. Barbiero, G. Manzi, P.A. Ferrari. - [s.l] : R foundation, 2015 Feb 27.

GenForImp: The Forward Imputation : A Sequential Distance-Based Approach for Imputing Missing Data

A. Barbiero;G. Manzi;P.A. Ferrari
2015

Abstract

Two methods based on the Forward Imputation approach are implemented for the imputation of quantitative missing data. One method alternates Nearest Neighbour Imputation and Principal Component Analysis (function 'ForImp.PCA'), the other uses Nearest Neighbour Imputation with the Mahalanobis distance (function 'ForImp.Mahala').
27-feb-2015
Mahalanobis distance; MCAR missing data; multivariate statistical analysis; nearest neighbour method; nonparametric methods; principal component analysis
Settore SECS-S/01 - Statistica
File in questo prodotto:
File Dimensione Formato  
GenForImp.pdf

accesso aperto

Tipologia: Altro
Dimensione 115.95 kB
Formato Adobe PDF
115.95 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/267250
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact