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