  This paper presents a hypothesis testing method given independent samples
from a number of connected populations. The method is motivated by a forestry
project for monitoring change in the strength of lumber. Traditional practice
has been built upon nonparametric methods which ignore the fact that these
populations are connected. By pooling the information in multiple samples
through a density ratio model, the proposed empirical likelihood method leads
to a more efficient inference and therefore reduces the cost in applications.
The new test has a classical chi-square null limiting distribution. Its power
function is obtained under a class of local alternatives. The local power is
found increased even when some underlying populations are unrelated to the
hypothesis of interest. Simulation studies confirm that this test has better
power properties than potential competitors, and is robust to model
misspecification. An application example to lumber strength is included.
