CRIS Current Research Information System
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
EnglishWe propose a method for imputing missing data by using conditional copula functions. Copulas are a powerful tool for multivariate analysis especially because they allow to i) fit any combination of marginal distribution functions, ii) model the marginal distributions and the dependence structure separately and iii) take into account complex dependence relationships. We present the method and perform a simulation study in order to compare it with two well–known imputation techniques: the regression imputation by EM algorithm and the nearest neighbour donor imputation. By varying different parameters we evaluate the performance of our proposal. Finally, we propose a generalization of our method by using non parametric estimation and inversion algorithms to generate random variates for conditional distributions.
| Titolo: | Exploring copulas for the imputation of missing nonlinearly dependent data | |
| Autore/i: | Bianchi G.; DI LASCIO, FRANCESCA MARTA LILJA; GIANNERINI, SIMONE; Manzari A.; Reale A.; Ruocco G. | |
| Autore/i Unibo: | ||
| Anno: | 2009 | |
| Titolo del libro: | Classification and Data Analysis 2009. Book of Short Papers | |
| Pagina iniziale: | 429 | |
| Pagina finale: | 432 | |
| Abstract: | We propose a method for imputing missing data by using conditional copula functions. Copulas are a powerful tool for multivariate analysis especially because they allow to i) fit any combination of marginal distribution functions, ii) model the marginal distributions and the dependence structure separately and iii) take into account complex dependence relationships. We present the method and perform a simulation study in order to compare it with two well–known imputation techniques: the regression imputation by EM algorithm and the nearest neighbour donor imputation. By varying different parameters we evaluate the performance of our proposal. Finally, we propose a generalization of our method by using non parametric estimation and inversion algorithms to generate random variates for conditional distributions. | |
| Data prodotto definitivo in UGOV: | 28-giu-2013 | |
| Appare nelle tipologie: | 4.01 Contributo in Atti di convegno |
File in questo prodotto:
Copyright © 2022