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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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