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Abstract This paper mainly discusses the asymptotic properties of quantile regression processes. In view of the exponential tightness and convexity argument, we prove the quantile regression estimators satisfy the functional… Click to show full abstract
Abstract This paper mainly discusses the asymptotic properties of quantile regression processes. In view of the exponential tightness and convexity argument, we prove the quantile regression estimators satisfy the functional moderate deviation principle. This method can be extended to a fair range of different statistical estimation problems such as quantile regression estimators with bridge penalized functions.
Keywords:
quantile regression;
regression;
regression processes;
deviations quantile;
moderate deviations
Journal Title: Communications in Statistics - Theory and Methods
Year Published: 2018
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Journal Title: Communications in Statistics - Theory and Methods
Year Published: 2018
Link to full text (if available)
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recommendations!