The problem of detecting changes in the distribution of alarmed
sensors is considered. Under a nonparametric change detection
framework, we present several detection and estimation algorithms
based on the Vapnik-Chervonenkis theory. Theoretical performance
guarantees are obtained by providing error exponents for
false-alarm and miss detection probabilities. Recursive algorithms
for the efficient computation of test statistics are derived. The
estimation problem is also considered in which, after detection is
made, the location with maximum distribution change is estimated.