Preprint Article Version 1 This version is not peer-reviewed

Regression Medians and Uniqueness

Version 1 : Received: 14 February 2020 / Approved: 15 February 2020 / Online: 15 February 2020 (14:51:15 CET)

A peer-reviewed article of this Preprint also exists.

Zuo, Y. Depth Induced Regression Medians and Uniqueness. Stats 2020, 3, 94-106. Zuo, Y. Depth Induced Regression Medians and Uniqueness. Stats 2020, 3, 94-106.

Journal reference: Stats 2020, 3, 9
DOI: 10.3390/stats3020009


Notion of median in one dimension is a foundational element in nonparametric statistics. It has been extended to multi-dimensional cases both in location and in regression via notions of data depth. Regression depth (RD) and projection regression depth (PRD) represent the two most promising notions in regression. Carrizosa depth DC is another depth notion in regression. Depth induced regression medians (maximum depth estimators) serve as robust alternatives to the classical least squares estimator. The uniqueness of regression medians is indispensable in the discussion of their properties and the asymptotics (consistency and limiting distribution) of sample regression medians. Are the regression medians induced from RD, PRD, and DC unique? Answering this question is the main goal of this article. It is found that only the regression median induced from PRD possesses the desired uniqueness property. The conventional remedy measure for non-uniqueness, taking average of all medians, might yield an estimator that no longer possesses the maximum depth in both RD and DC cases. These and other findings indicate that the PRD and its induced median are highly favorable among their leading competitors.

Subject Areas

uniqueness: regression depth; maximum depth estimator; regression median; robustness

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