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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/20865

Title: Local Influence Diagnostics for Incomplete Overdispersed Longitudinal Counts
Authors: Rakhmawati, Trias
Molenberghs, Geert
Verbeke, Geert
Faes, Christel
Issue Date: 2015
Citation: Journal of applied statistics, 43 (9), p. 1722-1737
Abstract: We develop local influence diagnostics to detect influential subjects when generalized linear mixed models are fitted to incomplete longitudinal overdispersed count data. The focus is on the influence stemming from the dropout model specification. In particular, the effect of small perturbations around an MAR specification are examined. The method is applied to data from a longitudinal clinical trial in epileptic patients. The effect on models allowing for overdispersion is contrasted with that on models that do not.
Notes: Molenberghs, G (reprint author), Univ Hasselt, I BioStat, Diepenbeek, Belgium. geert.molenberghs@uhasselt.be
URI: http://hdl.handle.net/1942/20865
Link to publication: https://www.researchgate.net/publication/295401290_Local_influence_diagnostics_for_incomplete_overdispersed_longitudinal_counts
DOI: 10.1080/02664763.2015.1117594
ISI #: 000375002600010
ISSN: 0266-4763
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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