caa a22 4500 60619634X CHVBK 20210128100923.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s10260-014-0287-2 doi (NATIONALLICENCE)springer-10.1007/s10260-014-0287-2 Exploring copulas for the imputation of complex dependent data [Elektronische Daten] [F. DiLascio, Simone Giannerini, Alessandra Reale] In this work we introduce a copula-based method for imputing missing data by using conditional density functions of the missing variables given the observed ones. In theory, such functions can be derived from the multivariate distribution of the variables of interest. In practice, it is very difficult to model joint distributions and derive conditional distributions, especially when the margins are different. We propose a natural solution to the problem by exploiting copulas so that we derive conditional density functions through the corresponding conditional copulas. The approach is appealing since copula functions enable us (1) to fit any combination of marginal distribution functions, (2) to take into account complex multivariate dependence relationships and (3) to model the marginal distributions and the dependence structure separately. We describe the method and perform a Monte Carlo study in order to compare it with two well-known imputation techniques: the nearest neighbour donor imputation and the regression imputation by EM algorithm. Our results indicate that the proposal compares favourably with classical methods in terms of preservation of microdata, margins and dependence structure. Springer-Verlag Berlin Heidelberg, 2014 Imputation nationallicence Copula function nationallicence Multivariate dependence nationallicence Donor imputation nationallicence EM-based regression imputation nationallicence DiLascio F. Faculty of Economics and Management, Free University of Bozen-Bolzano, Bolzano, Italy aut Giannerini Simone Department of Statistical Sciences, University of Bologna, Bologna, Italy aut Reale Alessandra ISTAT, Italian Statistical Institute, Rome, Italy aut Statistical Methods & Applications Springer Berlin Heidelberg 24/1(2015-03-01), 159-175 1618-2510 24:1<159 2015 24 10260 https://doi.org/10.1007/s10260-014-0287-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10260-014-0287-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- DiLascio F. Faculty of Economics and Management, Free University of Bozen-Bolzano, Bolzano, Italy aut NATIONALLICENCE 700 1- Giannerini Simone Department of Statistical Sciences, University of Bologna, Bologna, Italy aut NATIONALLICENCE 700 1- Reale Alessandra ISTAT, Italian Statistical Institute, Rome, Italy aut NATIONALLICENCE 773 0- Statistical Methods & Applications Springer Berlin Heidelberg 24/1(2015-03-01), 159-175 1618-2510 24:1<159 2015 24 10260