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SDS Seminar Series - Dr. Alessandro Rinaldo
Friday, February 28, 2020, 02:00pm - 03:00pm
Alessandro RinaldoTitle: "Change Point Analysis in High-dimensions"

Abstract: Statistical change point analysis is concerned with detecting and localizing abrupt changes in the data generating distribution in time series. A long-studied subject with a rich literature, change point analysis has produced a host of well-established methods for statistical inference available to practitioners. These techniques are used in diverse applications to address important real life problems, such as security monitoring, neuroimaging, ecological statistics and climate change, medical condition monitoring, sensor networks, risk assessment for disease outbreak, genetics and many others. Current frameworks for statistical analysis of change point problems often times rely on traditional modeling assumptions of parametric nature that are inadequate to capture the inherent complexity of modern, high-dimensional datasets. In this talk I will introduce various high-dimensional change point localization problems assuming independent observations: for univariate means, covariances and sparse networks. In each case, I will describe a phase transition in the space of the model parameters that separates parameter combinations for which the localization task is possible from those for which no consistent estimator of the change points exists. I will describe various algorithms for localization, which yield nearly minimax optimal rates in all cases under consideration. I will finally discuss a change point problem in fully nonparametric settings.
Location: CBA 4.324
February 28, 2020 - Dr. Alessandro Rinaldo
Carnegie Mellon University, Department of Statistics & Data Sciences
CBA 4.324, 2:00 to 3:00 PM