Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26351
Title: Empirical likelihood based diagnostics for heteroscedasticity in partial linear models
Authors: Wong, H 
Liu, F
Chen, M
Ip, WC
Issue Date: 2009
Publisher: Elsevier Science Bv
Source: Computational statistics and data analysis, 2009, v. 53, no. 9, p. 3466-3477 How to cite?
Journal: Computational Statistics and Data Analysis 
Abstract: In this paper, we propose a diagnostic technique for checking heteroscedasticity based on empirical likelihood for the partial linear models. We construct an empirical likelihood ratio test for heteroscedasticity. Also, under mild conditions, a nonparametric version of Wilk's theorem is derived, which says that our proposed test has an asymptotic chi-square distribution. Simulation results reveal that the finite sample performance of our proposed test is satisfactory in both size and power. An empirical likelihood bootstrap simulation is also conducted to overcome the size distortion in small sample sizes.
URI: http://hdl.handle.net/10397/26351
DOI: 10.1016/j.csda.2009.02.029
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