Abstract
In this note we evaluate the properties and performance of a censored median regression estimator, as presented in literature by different authors in the context of support vector regression. This estimator is based on minimisation of an inequality constrained loss in a linear program formulation. Using a theoretical argument, we conjecture that the estimator is not consistent, and we compare its performance on simulated and real data in the one-sample case, with the Kaplan-Meier estimator and an inverse probability weighted estimator. We also compare the performance of the estimator on simulated and real data in the censored median regression setting, with the Portnoy estimator and the inverse probability weighted estimator.
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Notes
- 1.
The partial derivatives in the positive and negative directions.
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Eleuteri, A. (2016). A Commentary on a Censored Regression Estimator. In: Angelini, C., Rancoita, P., Rovetta, S. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2015. Lecture Notes in Computer Science(), vol 9874. Springer, Cham. https://doi.org/10.1007/978-3-319-44332-4_1
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DOI: https://doi.org/10.1007/978-3-319-44332-4_1
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