The impact of incomplete data on quantile regression for longitudinal data

Anneleen Verhasselt; Alvaro J Florez; Ingrid Van Keilegom; Geert  Molenberghs

The impact of incomplete data on quantile regression for longitudinal data

Authors

Anneleen Verhasselt Data Science Institute, I-Biostat, Universiteit Hasselt, Diepenbeek, Belgium
Alvaro J Florez Data Science Institute, I-Biostat, Universiteit Hasselt, Diepenbeek, Belgium. School of Statistics, Universidad del Valle, Cali, Colombia
Ingrid Van Keilegom Research Centre for Operations Research and Statistics, KU Leuven, Leuven, Belgium
Geert Molenberghs Data Science Institute, I-Biostat Universiteit Hasselt, Diepenbeek, Belgium. I-Biostat, KU Leuven, Leuven, Belgium

Keywords:

Dropout; Inverse probability weighting; Missing data; Multiple imputation; Quantile regression

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 weighting. 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.

Journal of Statistical Research 2021, Vol. 55, No. 1, pp. 43-58

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Published

2021-12-09

How to Cite

Verhasselt, A., Florez, A. J., Keilegom, I. V., & Molenberghs, G. . (2021). The impact of incomplete data on quantile regression for longitudinal data. Journal of Statistical Research, 55(1), 43–58. Retrieved from https://banglajol.info/index.php/JStR/article/view/56566