Please use this identifier to cite or link to this item: https://doi.org/10.1007/s11749-013-0343-5
Title: Empirical likelihood for least absolute relative error regression
Authors: Li, Z.
Lin, Y.
Zhou, G.
Zhou, W. 
Keywords: Empirical likelihood
Multiplicative regression model
Relative error estimation
Issue Date: 2014
Citation: Li, Z., Lin, Y., Zhou, G., Zhou, W. (2014). Empirical likelihood for least absolute relative error regression. Test 23 (1) : 86-99. ScholarBank@NUS Repository. https://doi.org/10.1007/s11749-013-0343-5
Abstract: Multiplicative regression models are useful for analyzing data with positive responses, such as wages, stock prices and lifetimes, that are particularly common in economic, financial, epidemiological and social studies. Recently, the least absolute relative error (LARE) estimation was proposed to be a useful alternative to the conventional least squares (LS) or least absolute deviation (LAD). However, one may resort to the time-consuming resampling methods for the inference of the LARE estimation. This paper proposes an empirical likelihood approach towards constructing confidence intervals/regions of the regression parameters for the multiplicative models. The major advantage of the proposal is its ability of internal studentizing to avoid density estimation. And it is computationally fast. Simulation studies investigate the effectiveness of the proposed method. An analysis of the body fat data is presented to illustrate the new method. © 2013 Sociedad de Estadística e Investigación Operativa.
Source Title: Test
URI: http://scholarbank.nus.edu.sg/handle/10635/125052
ISSN: 11330686
DOI: 10.1007/s11749-013-0343-5
Appears in Collections:Staff Publications

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