Finding Differentially Covarying Needles in a Temporally Evolving Haystack: A Scan Statistics Perspective

20 Nov 2017Ronak MehtaHyunwoo J. KimShulei WangSterling C. JohnsonMing YuanVikas Singh

Recent results in coupled or temporal graphical models offer schemes for estimating the relationship structure between features when the data come from related (but distinct) longitudinal sources. A novel application of these ideas is for analyzing group-level differences, i.e., in identifying if trends of estimated objects (e.g., covariance or precision matrices) are different across disparate conditions (e.g., gender or disease)... (read more)

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