Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/37066
Title: The impact of incomplete data on quantile regression for longitudinal data
Authors: VERHASSELT, Anneleen 
FLOREZ POVEDA, Alvaro 
Van Keilegeom, Ingrid
MOLENBERGHS, Geert 
Issue Date: 2021
Source: Journal of Statistical Research, 55 (1) , p. 43 -58
Abstract: We investigate the performance of methods for estimating the conditional quantile of a response based on longitudinal data, when outcomes are incomplete and when the correlation between repeated responses is ignored. In a simulation study, we compare the performance of the quantile regression estimator based on the complete cases, the available cases, quantile-based multiple imputation, and quantile-based inverse probability weight-ing. In the data setting considered, quantile-based multiple imputation is the most promising method with the best bias-efficiency trade-off. A potential drawback, however, is its computation time.
Keywords: and phrases: Dropout;Inverse probability weighting;Missing data;Multiple imputation;Quantile regression
Document URI: http://hdl.handle.net/1942/37066
DOI: 10.47302/jsr.2021550105
Category: A2
Type: Journal Contribution
Appears in Collections:Research publications

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