Biostatistics Special Seminar

Abstract: "Testing for the presence of autocorrelation is a fundamental problem in time series analysis. Classical methods such as the Box–Pierce test rely on the assumption of stationarity, necessitating the removal of non-stationary components such as trends or shifts in the mean prior to application. However, this is not always practical, particularly when the mean structure is complex, such as being piecewise constant with frequent mean shifts. In this talk, we introduce a new inferential framework for autocorrelation for time series data under frequent mean shifts. We introduced a generalized Box test that reliably tests for autocorrelation and is robust against mean shifts. We illustrate an application of our method to nanopore data."

About the speaker: Ning Hao is an associate professor in the Department of Mathematics at the University of Arizona. His research interests broadly span statistics and data science, with specific focus areas including high-dimensional data analysis, statistical learning, change-point detection, and their applications in various scientific domains. He is currently an Editor for the journal STAT and an Associate Editor for the Journal of the American Statistical Association.

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Meeting ID: 944 4053 0193