Handling the effect of non-response in graphical models for longitudinal data
Handling the effect of non-response in graphical models for longitudinal data
In this paper we investigated the use of attrition weights to cope with non-response 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.
Graphical chains, weighting, MAR, parametric bootstrap
109-123
Borgoni, Riccardo
df9c90ab-c2d2-47d6-bcc7-1444a605d6ff
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Berrington, Ann M.
bd0fc093-310d-4236-8126-ca0c7eb9ddde
March 2009
Borgoni, Riccardo
df9c90ab-c2d2-47d6-bcc7-1444a605d6ff
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Berrington, Ann M.
bd0fc093-310d-4236-8126-ca0c7eb9ddde
Borgoni, Riccardo, Smith, Peter W.F. and Berrington, Ann M.
(2009)
Handling the effect of non-response in graphical models for longitudinal data.
Statistical Methods & Applications, 18 (1), .
(doi:10.1007/s10260-008-0093-9).
Abstract
In this paper we investigated the use of attrition weights to cope with non-response 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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Published date: March 2009
Keywords:
Graphical chains, weighting, MAR, parametric bootstrap
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Local EPrints ID: 150775
URI: http://eprints.soton.ac.uk/id/eprint/150775
ISSN: 1618-2510
PURE UUID: 1492bbac-61b0-422f-a361-df1e28640517
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Date deposited: 07 May 2010 08:39
Last modified: 14 Mar 2024 02:37
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Author:
Riccardo Borgoni
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