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Abstract Quantile regression for right‐ or left‐censored outcomes has attracted attention due to its ability to accommodate heterogeneity in regression analysis of survival times. Rank‐based inferential methods have desirable properties for quantile regression analysis, but censored data poses challenges to the general concept of ranking. In this article, we propose a notion of censored quantile regression rank scores, which enables us to construct rank‐based tests for quantile regression coefficients at a single quantile or over a quantile region. A model‐based bootstrap algorithm is proposed to implement the tests. We also illustrate the advantage of focusing on a quantile region instead of a single quantile level when testing the effect of certain covariates in a quantile regression framework.
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A Quantile-based Nested Partition Algorithm for Black-box Functions on a Continuous Domain
Simulation models commonly describe complex systems with no closed-form analytical representation. This paper proposes an algorithm for functions on continuous domains that fits into the nested partition framework and uses quantile estimation to rank regions and identify the most promising region. Additionally, we apply the optimal computational budget allocation (OCBA) method for allocating sample points using the normality property of quantile estimators. We prove that, for functions satisfying the Lipschitz condition, the algorithm converges in probability to a region that contains the true global optimum. The paper concludes with some numerical results.
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- Award ID(s):
- 1632793
- PAR ID:
- 10036540
- Date Published:
- Journal Name:
- Proceedings of the 2016 Winter Simulation Conference / 978-1-5090-4484-9
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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