A branch-and-bound algorithm for instrumental variable quantile regression
- This paper studies a statistical problem called instrumental variable quantile regression (IVQR). We model IVQR as a convex quadratic program with complementarity constraints and—although this type of program is generally NPhard—we develop a branch-and-bound algorithm to solve it globally. We also derive bounds on key variables in the problem, which are valid asymptotically for increasing sample size. We compare our method with two well known global solvers, one of which requires the computed bounds. On random instances, our algorithm performs well in terms of both speed and robustness.
Metadaten| Author: | Guanglin Xu, Samuel Burer |
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| DOI: | https://doi.org/10.1007/s12532-017-0117-2 |
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| ISSN: | 1867-2949 |
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| Parent Title (English): | Mathematical Programming Computation |
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| Publisher: | Springer Science and Business Media LLC |
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| Document Type: | Article |
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| Language: | English |
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| Year of Completion: | 2017 |
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| Tag: | Software; Theoretical Computer Science |
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| Volume: | 9 |
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| Issue: | 4 |
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| Page Number: | 27 |
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| First Page: | 471 |
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| Last Page: | 497 |
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| Mathematical Programming Computation : | MPC 2017 - Issue 4 |
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