A class for `PerfMeasure`

and its extensions

Objects can be created by calls of the form `new("PerfMeasure", ...)`

.

`MARE`

:Object of class

`"numeric"`

. The mean (on the replications performed) of the absolute relative error between the imputed and the corresponding original value.`RB`

:Object of class

`"numeric"`

. The relative bias of the estimator for the dependence parameter.`RRMSE`

:Object of class

`"numeric"`

. The relative root mean squared error of the estimator for the dependence parameter.

`TID`

:Object of class

`"vector"`

. Upper and lower tail dependence indexes for bivariate copulas. Original function is in`tailIndex`

.

- show
`signature(object = "PerfMeasure")`

: ...

Francesca Marta Lilja Di Lascio <marta.dilascio@unibz.it>,

Simone Giannerini <simone.giannerini@unibo.it>

Di Lascio, F.M.L. Giannerini, S. and Reale A. (201x) "A multivariate technique based on conditional copula specification for the imputation of complex dependent data". Working paper.

Di Lascio, F.M.L., Giannerini, S. and Reale, A. (2015) "Exploring Copulas for the Imputation of Complex Dependent Data". Statistical Methods & Applications, 24(1), p. 159-175. DOI 10.1007/s10260-014-0287-2.

Di Lascio, F.M.L., Giannerini, S. and Reale, A. (2014) "Imputation of complex dependent data by conditional copulas: analytic versus semiparametric approach", Book of proceedings of the 21st International Conference on Computational Statistics (COMPSTAT 2014), p. 491-497. ISBN 9782839913478.

Bianchi, G. Di Lascio, F.M.L. Giannerini, S. Manzari, A. Reale, A. and Ruocco, G. (2009) "Exploring copulas for the imputation of missing nonlinearly dependent data". Proceedings of the VII Meeting Classification and Data Analysis Group of the Italian Statistical Society (Cladag), Editors: Salvatore Ingrassia and Roberto Rocci, Cleup, p. 429-432. ISBN: 978-88-6129-406-6.

See Also `CoImp`

, `lp`

and `copula`

.

1 | ```
showClass("PerfMeasure")
``` |

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