  Jackknife empirical likelihood (JEL) is an effective modified version of
empirical likelihood method (EL). Through the construction of the jackknife
pseudo-values, JEL overcomes the computational difficulty of EL method when its
constraints are nonlinear while maintaining the same asymptotic results for one
sample and two-sample U statistics. In this paper, we propose an adjusted
version of JEL to guarantee that the adjusted jackknife empirical likelihood
(AJEL) statistic is well-defined for all the values of the parameter, instead
of restricting on the convex hull of the estimation equation. The properties of
JEL have been preserved for AJEL.
