A procedure based on the use of Sequential Regression and Classification Trees for the imputation of missing data is proposed. We dealt with the case of non-monotone patterns of missing data with mixed measurement level of the variables. The aim of the analysis is to obtain a completed data matrix with optimal characteristics (with respect to means, variances and correlations of the variables) which is often the main demand for a statistical office. Moreover we want to obtain a measure of the additional variability due to the presence of missing values. A simulation case with qualitative and quantitative data is analyzed and the results compared with other procedures. In particular, the performance of Multiple Imputation, using IVEWARE, were compared with our proposal using a large simulation with artificial data and with the EU-SILC cross-sectional data. Our non-parametric method showed to be very competitive on these simulations.

Bootstrap and Nonparametric Predictors to Impute Missing Data / DI CIACCIO, Agostino. - STAMPA. - (2008), pp. 147-150. (Intervento presentato al convegno First Joint meeting of SFC and CLADAG tenutosi a Caserta nel 11-13 Giugno 2008).

Bootstrap and Nonparametric Predictors to Impute Missing Data

DI CIACCIO, AGOSTINO
2008

Abstract

A procedure based on the use of Sequential Regression and Classification Trees for the imputation of missing data is proposed. We dealt with the case of non-monotone patterns of missing data with mixed measurement level of the variables. The aim of the analysis is to obtain a completed data matrix with optimal characteristics (with respect to means, variances and correlations of the variables) which is often the main demand for a statistical office. Moreover we want to obtain a measure of the additional variability due to the presence of missing values. A simulation case with qualitative and quantitative data is analyzed and the results compared with other procedures. In particular, the performance of Multiple Imputation, using IVEWARE, were compared with our proposal using a large simulation with artificial data and with the EU-SILC cross-sectional data. Our non-parametric method showed to be very competitive on these simulations.
2008
9788849516562
File allegati a questo prodotto
Non ci sono file associati a questo prodotto.

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/11573/198018
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact