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Parametric models for incomplete continuous and categorical longitudinal data

Kenward, Michael G. and Molenberghs, G. (1999) Parametric models for incomplete continuous and categorical longitudinal data. Review of: Parametric models for incomplete continuous and categorical longitudinal data. by Kenward, Michael G. and Molenberghs, G.. Statistical Methods in Medical Research, 8 (1). pp. 51-83. ISSN 0962-2802. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:16888)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL
http://smm.sagepub.com/cgi/content/refs/8/1/51

Abstract

This paper reviews models for incomplete continuous and categorical longitudinal data. In terms of Rubin's classification of missing value processes we are specifically concerned with the problem of nonrandom missingness. A distinction is drawn between the classes of selection and pattern-mixture models and, using several examples, these approaches are compared and contrasted. The central roles of identifiability and sensitivity are emphasized throughout.

Item Type: Review
Subjects: Q Science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Divisions > Division for the Study of Law, Society and Social Justice > School of Social Policy, Sociology and Social Research
Depositing User: I.T. Ekpo
Date Deposited: 20 Jun 2009 05:20 UTC
Last Modified: 16 Nov 2021 09:54 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/16888 (The current URI for this page, for reference purposes)
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