Change-point Estimation via Empirical Likelihood for a Segmented Linear Regression
by Zhihua Liu and Lianfen Qian.
For a segmented regression system with an unknown change-point
over two domains of a predictor, a new empirical likelihood ratio
statistic is proposed to test the null hypothesis of no change.
Under the null hypothesis of no change, the proposed test
statistic is empirically shown asymptotically Gumbel distributed
with robust location and scale parameters against various
parameter settings and error distributions. Under the alternative
hypothesis with a change-point, the test statistic is utilized to
estimate the change point between the two domains. The power
analysis shows that the proposed test is tractable. An empirical
example on analyzing the plasma osmolality data is given.
Empirical likelihood ratio, Gumbel
extreme value distribution, segmented linear regression,
Zhihua Liu, firstname.lastname@example.org
Lianfen Qian, email@example.com
Suojin Wang, sjwang@PICARD.tamu.edu
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