Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26675
Title: Empirical likelihood based diagnostics for heteroscedasticity in partially linear errors-in-variables models
Authors: Wong, H 
Liu, F
Chen, M
Ip, WC
Keywords: Empirical likelihood ratio
Errors-in-variables
Heteroscedasticity
Nuisance parameter
Partially linear models
Issue Date: 2009
Publisher: Elsevier Science Bv
Source: Journal of statistical planning and inference, 2009, v. 139, no. 3, p. 916-929 How to cite?
Journal: Journal of Statistical Planning and Inference 
Abstract: A standard assumption in regression analysis is homogeneity of the error variance. Violation of this assumption can have adverse consequences for the efficiency of estimators. In this paper, we propose an empirical likelihood based diagnostic technique for heteroscedasticity in the partially linear errors-in-variables models. Under mild conditions, a nonparametric version of Wilk's theorem is derived. Simulation results reveal that our test performs well in both size and power.
URI: http://hdl.handle.net/10397/26675
DOI: 10.1016/j.jspi.2008.05.049
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