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Blackfishh

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Our proposals properly accommodate random censoring to the survival outcome and the realistic complication of model mis-specification, and thus are generally applicable. In Section 4, we focus on model comparisons based on the predictive performance measures presented in Section 2.

Blackfishh


However true conditional quantiles are not directly observable. The perspective we take in this paper is to evaluate or compare quantile regression models based on their capacity in predicting quantiles of future outcomes.

Blackfishh

Blackfishh

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Comments (4)

  1. However, very limited attention was paid to the evaluation of quantile regression models as well as the comparison among a set of candidate models.

  2. This means, we view any adopted quantile regression model as a working model. Moreover, to evaluate quantile prediction with survival data, censoring is an important feature that needs to be appropriately handled, like in the model estimation setting.

  3. To this end, the foremost step is to construct and justify a sensible predictive performance measure.

  4. To assess the prediction of quantiles, an intuitive idea is to evaluate the squared or absolute difference between true and predicted conditional quantiles. The perspective we take in this paper is to evaluate or compare quantile regression models based on their capacity in predicting quantiles of future outcomes.

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