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Inference in Constrained Quantile Regression Parker, Tom
Description
I investigate the asymptotic distribution of linear quantile regression coefficient estimates when the parameter may lie on the boundary of the parameter space, and related inference procedures when the null hypothesis asserts that the parameters lie on a boundary of this set. Particular attention is paid to parameter spaces defined by sets of linear inequalities. I provide a uniform characterization of the constrained quantile regression process over an interval of quantile levels. This asymptotic theory is used to derive asymptotic inference methods for three related processes based on the constrained quantile regression process.
Item Metadata
Title |
Inference in Constrained Quantile Regression
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Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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Date Issued |
2018-02-01T09:49
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Description |
I investigate the asymptotic distribution of linear quantile regression coefficient estimates when the parameter may lie on the boundary of the parameter space, and related inference procedures when the null hypothesis asserts that the parameters lie on a boundary of this set. Particular attention is paid to parameter spaces defined by sets of linear inequalities. I provide a uniform characterization of the constrained quantile regression process over an interval of quantile levels. This asymptotic theory is used to derive asymptotic inference methods for three related processes based on the constrained quantile regression process.
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Extent |
33 minutes
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Subject | |
Type | |
File Format |
video/mp4
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Language |
eng
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Notes |
Author affiliation: University of Waterloo
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Series | |
Date Available |
2018-07-31
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0369266
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URI | |
Affiliation | |
Peer Review Status |
Unreviewed
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Scholarly Level |
Researcher
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International