The impact of incomplete data on quantile regression for longitudinal dataDownload PDF

10 Jun 2020 (modified: 13 Jul 2020)ICML 2020 Workshop Artemiss SubmissionReaders: Everyone
  • Keywords: Inverse probability weighting, Longitudinal data, Missing data, Pseudo-likelihood, Quantile regression
  • 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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