ML Anthology

The Impact of Incomplete Data on Quantile Regression for Longitudinal Data

Anneleen Verhasselt, Alvaro José Flórez, Ingrid Van Keilegom, Geert Molenberghs
ICMLW 2020
/icmlw/2020/verhasselt2020icmlw-impact/

Abstract

We investigate the performance of quantile methods for longitudinal data with missingness. In a simulation study, we compare the performance of the quantile regression using different alternatives for handling missing data and taking the correlation into account. As expected, the non-likelihood-based methods provide biased estimates under the missing at random assumption. On the other hand, an inverse probability weighting approach corrects for biasedness.

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Cite

Text

Verhasselt et al. "The Impact of Incomplete Data on Quantile Regression for Longitudinal Data." ICML 2020 Workshops: Artemiss, 2020.

Markdown

[Verhasselt et al. "The Impact of Incomplete Data on Quantile Regression for Longitudinal Data." ICML 2020 Workshops: Artemiss, 2020.](https://mlanthology.org/icmlw/2020/verhasselt2020icmlw-impact/)

BibTeX

@inproceedings{verhasselt2020icmlw-impact,
  title     = {{The Impact of Incomplete Data on Quantile Regression for Longitudinal Data}},
  author    = {Verhasselt, Anneleen and Flórez, Alvaro José and Van Keilegom, Ingrid and Molenberghs, Geert},
  booktitle = {ICML 2020 Workshops: Artemiss},
  year      = {2020},
  url       = {https://mlanthology.org/icmlw/2020/verhasselt2020icmlw-impact/}
}