Vine Copulas for Imputation of Monotone Non-response

Monotone patterns of non-response may occur in longitudinal studies. When the measured variables are dependent, it is beneficial to use their joint statistical model to impute the missing values. We propose to use vine copulas to factorise the density of the observed variables into a cascade of biva...

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Bibliographic Details
Published in:International statistical review 2018-12, Vol.86 (3), p.488-511
Main Authors: Hasler, Caren, Craiu, Radu V., Rivest, Louis-Paul
Format: Article
Language:English
Subjects:
Bivariate analysis
copula
Correlation analysis
Dependent variables
D‐vine
imputation
non‐response
Parameter estimation
Statistical models
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Summary:Monotone patterns of non-response may occur in longitudinal studies. When the measured variables are dependent, it is beneficial to use their joint statistical model to impute the missing values. We propose to use vine copulas to factorise the density of the observed variables into a cascade of bivariate copulas that yield a flexible model of their joint distribution. The structure of the vine depends on the non-response pattern. We propose a method to select the model, to estimate the parameters of the bivariate copulas of the selected model and to impute using the constructed model. The imputed values are drawn from the conditional distribution of the missing values, given the observed data. We discuss the generalisation of our results to more global non-response patterns.
ISSN:0306-7734
1751-5823
DOI:10.1111/insr.12263