  The problem of sequential change diagnosis is considered, where observations
are obtained on-line, an abrupt change occurs in their distribution, and the
goal is to quickly detect the change and accurately identify the post-change
distribution, while controlling the false alarm rate. A finite set of
alternatives is postulated for the post-change regime, but no prior information
is assumed for the unknown change-point. A drawback of many algorithms that
have been proposed for this problem is the implicit use of pre-change data for
determining the post-change distribution. This can lead to very large
conditional probabilities of misidentification, given that there was no false
alarm, unless the change occurs soon after monitoring begins. A novel,
recursive algorithm is proposed and shown to resolve this issue without the use
of additional tuning parameters and without sacrificing control of the
worst-case delay in Lorden's sense. A theoretical analysis is conducted for a
general family of sequential change diagnosis procedures, which supports the
proposed algorithm and revises certain state-of-the-art results. Additionally,
a novel, comprehensive method is proposed for the design and evaluation of
sequential change diagnosis algorithms. This method is illustrated with
simulation studies, where existing procedures are compared to the proposed.
