  Minimax robust decentralized detection is studied for parallel sensor
networks. Random variables corresponding to sensor observations are assumed to
follow a distribution function, which belongs to an uncertainty class. It has
been proven that, for some uncertainty classes, if all probability
distributions are absolutely continuous with respect to a common measure, the
joint stochastic boundedness property, which is the fundamental rule for the
derivations in Veerevalli's work, does not hold. This raises a natural question
whether minimax robust decentralized detection is possible if the uncertainty
classes do not own this property. The answer to this question has been shown to
be positive, which leads to a generalization of the work of Veerevalli.
Moreover, due to a direct consequence of Tsitsiklis's work, quantization
functions at the sensors are not required to be monotone. For the proposed
model, some specific examples have been provided and possible generalizations
have been discussed.
