In this paper we investigated the use of attrition weights to cope with nonresponse when selecting graphical chain models for longitudinal data. We proposed a parametric bootstrap approach to account for the extra variability introduced by the estimation of the weights and compared this with results using standard test procedures.
Borgoni, R., Berrington, S., Smith, P. (2009). Handling the effect of non-response in graphical models for longitudinal data. STATISTICAL METHODS & APPLICATIONS, 18(1), 109-123 [10.1007/s10260-008-0093-9].
Handling the effect of non-response in graphical models for longitudinal data
BORGONI, RICCARDO;
2009
Abstract
In this paper we investigated the use of attrition weights to cope with nonresponse when selecting graphical chain models for longitudinal data. We proposed a parametric bootstrap approach to account for the extra variability introduced by the estimation of the weights and compared this with results using standard test procedures.
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