Imputation algorithm using copulas

Abstract

In this paper the author demonstrates how the copulas approach can be used to find algorithms for imputing dropouts in repeated measurements studies. One problem with repeated measurements is the knowledge that the data is describedby joint distribution. Copulas are used to create the joint distribution with given marginal distributions. Knowing the joint distributionwe can find the conditional distribution of the measurement at a specific time point, conditioned by past measurements, and this will be essential for imputing missing values. Using Gaussian copulas, two simple methods for imputation are presented. Compound symmetry and the case of autoregressive dependencies are discussed. Effectiveness of the proposed approach is tested via series of simulations and results showing that the imputation algorithms based on copulas are appropriate for modelling dropouts.

Keywords

No keyword data available

Data

Language:	English
Year of publishing:	2006
Typology:	1.01 - Original Scientific Article
Organization:	UL FDV - Faculty of Social Sciences
Publisher:	Fakulteta za družbene vede
UDC:	303
COBISS:	25331293
ISSN:	1854-0023
Views:	557
Downloads:	39
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Unknown
URN:	URN:NBN:SI
Type (COBISS):	Not categorized
Pages:	str. 109-120
Volume:	ǂVol. ǂ3
Issue:	ǂno. ǂ1
Chronology:	2006
Keywords (UDC):	social sciences;družbene vede;methods of the social sciences;metode družbenih ved;
ID:	40638