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Title: | Iterative Multiple Imputation: A Framework to Determine the Number of Imputed Datasets | Authors: | NASSIRI, Vahid MOLENBERGHS, Geert VERBEKE, Geert Barbosa-Breda, Joao |
Issue Date: | 2020 | Publisher: | AMER STATISTICAL ASSOC | Source: | AMERICAN STATISTICIAN, 74 (2) , p. 125 -136 | Abstract: | We consider multiple imputation as a procedure iterating over a set of imputed datasets. Based on an appropriate stopping rule the number of imputed datasets is determined. Simulations and real-data analyses indicate that the sufficient number of imputed datasets may in some cases be substantially larger than the very small numbers that are usually recommended. For an easier use in various applications, the proposed method is implemented in the R package imi. | Notes: | Nassiri, V (reprint author), Katholieke Univ Leuven, I BioStat, B-3000 Louvain, Belgium. vahid.nassiri@kuleuven.be |
Other: | Nassiri, V (corresponding author), Katholieke Univ Leuven, I BioStat, B-3000 Louvain, Belgium. vahid.nassiri@kuleuven.be | Keywords: | Central limit theorem;Incomplete data;Iterative procedure;Missing data | Document URI: | http://hdl.handle.net/1942/31371 | ISSN: | 0003-1305 | e-ISSN: | 1537-2731 | DOI: | 10.1080/00031305.2018.1543615 | ISI #: | WOS:000530956200004 | Rights: | 2020 Informa UK Limited. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2021 |
Appears in Collections: | Research publications |
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