  Population quantiles and their functions are important parameters in many
applications. For example, the lower quantiles often serve as crucial quality
indices for forestry products. Given several independent samples from
populations satisfying the density ratio model, we investigate the properties
of empirical likelihood (EL) based inferences. The induced EL quantile
estimators are shown to admit a Bahadur representation that leads to
asymptotically valid confidence intervals for functions of quantiles. We
rigorously prove that EL quantiles based on all the samples are more efficient
than empirical quantiles based on individual samples. A simulation study shows
that the EL quantiles and their functions have superior performance when the
density ratio model assumption is satisfied and when it is mildly violated. An
example is used to demonstrate the new method and the potential cost savings.
