Analysis of least absolute deviation

Kani Chen, Zhiliang Ying, Hong Zhang and Lincheng Zhao

Biometrika, 2008, vol. 95, issue 1, 107-122

Abstract: We develop a unified L 1 -based analysis-of-variance-type method for testing linear hypotheses. Like the classical L 2 -based analysis of variance, the method is coordinate-free in the sense that it is invariant under any linear transformation of the covariates or regression parameters. Moreover, it allows singular design matrices and heterogeneous error terms. A simple approximation using stochastic perturbation is proposed to obtain cut-off values for the resulting test statistics. Both test statistics and distributional approximations can be computed using standard linear programming. An asymptotic theory is derived for the method. Special cases of one- and multi-way analysis of variance and analysis of covariance models are worked out in detail. The main results of this paper can be extended to general quantile regression. Extensive simulations show that the method works well in practical settings. The method is also applied to a dataset from General Social Surveys. Copyright 2008, Oxford University Press.

Date: 2008
References: Add references at CitEc
Citations: View citations in EconPapers (23)

Downloads: (external link)
http://hdl.handle.net/10.1093/biomet/asm082 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:biomet:v:95:y:2008:i:1:p:107-122

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

Biometrika is currently edited by Paul Fearnhead

More articles in Biometrika from Biometrika Trust Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK.
Bibliographic data for series maintained by Oxford University Press ().