Clustering of large deviations in moving average processes: short and long memory regimes Date: 23 March 2023, Thursday Time: 10am AEDT Statistics and Stochastic colloquium (part of the Colloquium Series of the Department of Mathematics and Statistics) at La Trobe University jointly organized with the Probability Victoria Seminar. Contact the organizers: Kostya Borovkov kostya.borovkov@gmail.com, Andriy Olenko a.olenko@latrobe.edu.au Speaker: Prof Gennady Samorodnitsky (Cornell University, United States of America) Abstract: We describe the cluster of large deviations events that arise when one such large deviations event occurs. We work in the framework of an infinite moving average process with a noise that has finite exponential moments. The cluster turns out to have different shapes in the cases when the moving average process has short memory and long memory. Joint work with Arijit Chakrabarty. Zoom meeting link: https://unimelb.zoom.us/j/88379660402?pwd=bzh6WUM3UFR5dUhnVj
Valid inference after clustering with application to single-cell RNA-sequencing data Speaker: Lucy Gao, University of British Columbia Zoom link: https://uni-sydney.zoom.us/j/8695451737 Abstract: In single-cell RNA-sequencing studies, researchers often model the variation between cells with a latent variable, such as cell type or pseudotime, and investigate associations between the genes and the latent variable. As the latent variable is unobserved, a two-step procedure seems natural: first estimate the latent variable, then test the genes for association with the estimated latent variable. However, if the same data are used for both of these steps, then standard methods for computing p-values in the second step will fail to control the type I error rate. In my talk, I will introduce two different approaches to this problem. First, I will apply ideas from selective inference to develop a valid test for a difference in means between clusters obtained from the hierarchical clustering alg