Imputation algorithm using copulas

Käärik, Ene (author)

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.

• Language: English
• Work type: Not categorized
• Typology: 1.01 - Original Scientific Article
• Organization: FDV - Faculty of Social Sciences
• Year: 2006
• Number of pages: Str. 109-120
• Numbering: Vol. 3, no. 1
• PID: 20.500.12556/RUL-8444
• UDC: 303
• ISSN on article: 1854-0023
• COBISS.SI-ID: 25331293
• Publication date in RUL: 11.07.2014

KÄÄRIK, Ene, 2006, Imputation algorithm using copulas. Advances in methodology and statistics [online]. 2006. Vol. 3, no. 1, p. 109–120. [Accessed 12 June 2025]. Retrieved from: http://mrvar.fdv.uni-lj.si/pub/mz/mz3.1/kaarik.pdf

Record is a part of a journal

• Title: Advances in methodology and statistics
• Shortened title: Metodol. zv.
• Publisher: Fakulteta za družbene vede
• ISSN: 1854-0023
• COBISS.SI-ID: 215795712