  In many applications such as copy number variant (CNV) detection, the goal is
to identify short segments on which the observations have different means or
medians from the background. Those segments are usually short and hidden in a
long sequence, and hence are very challenging to find. We study a super
scalable short segment (4S) detection algorithm in this paper. This
nonparametric method clusters the locations where the observations exceed a
threshold for segment detection. It is computationally efficient and does not
rely on Gaussian noise assumption. Moreover, we develop a framework to assign
significance levels for detected segments. We demonstrate the advantages of our
proposed method by theoretical, simulation, and real data studies.
