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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/464

Title: Sensitivity analysis for incomplete contingency tables: the Slovenian plebiscite case
Authors: Molenberghs, Geert
Kenward, Michael G.
Goetghebeur, Els J.T.
Keywords: Missing data
Longitudinal data
Issue Date: 2001
Publisher: BLACKWELL PUBL LTD
Citation: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 50(1). p. 15-29
Abstract: Classical inferential procedures induce conclusions from a set of data to a population of interest, accounting for the imprecision resulting from the stochastic component of the model. Less attention is devoted to the uncertainty arising from (unplanned) incompleteness in the data. Through the choice of an identifiable model for non-ignorable non-response, one narrows the possible data-generating mechanisms to the point where inference only suffers from imprecision. Some proposals have been made for assessing the sensitivity to these modelling assumptions; many are based on fitting several plausible but competing models. For example, we could assume that the missing data are missing at ranom in one model, and than fit an additional model where non-random missingness is assumed. On the bais of data from a Slovenian plebiscite, conducte in 1991, to prepare for independence, it is shown that such an ad hoc procedure may be misleading. We propose an approach which identifies and incorporates both sources of uncertainty in inference: imprecision due to finite sampling and ignorance due to incompleteness. A simple sensitivity analysis considers a finite set of plausible models. We take this idea one step further by considering more degrees of freedom than the data support. This produces sets of estimates (regions of ignorance) and sets of confidence regions(combined into regions of uncertainty).
URI: http://hdl.handle.net/1942/464
Link to publication: https://www.academia.edu/8393366/Sensitivity_analysis_for_incomplete_contingency_tables_the_Slovenian_plebiscite_case
DOI: 10.1111/1467-9876.00217
ISI #: 000166938100002
ISSN: 0035-9254
Category: A1
Type: Journal Contribution
Validation: ecoom, 2002
Appears in Collections: Research publications

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