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http://hdl.handle.net/1942/20865
Title: | Local Influence Diagnostics for Incomplete Overdispersed Longitudinal Counts | Authors: | RAKHMAWATI, Trias Wahyuni MOLENBERGHS, Geert VERBEKE, Geert FAES, Christel |
Issue Date: | 2015 | Source: | 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 | Keywords: | combined model; missing data; Poisson–Gamma–Normal model; Poisson–Normal model; sensitivity analysis | Document URI: | http://hdl.handle.net/1942/20865 | Link to publication/dataset: | https://www.researchgate.net/publication/295401290_Local_influence_diagnostics_for_incomplete_overdispersed_longitudinal_counts | ISSN: | 0266-4763 | e-ISSN: | 1360-0532 | DOI: | 10.1080/02664763.2015.1117594 | ISI #: | 000375002600010 | Rights: | © 2016 Taylor & Francis | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2017 |
Appears in Collections: | Research publications |
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