STATSCALE SEMINARS
CURRENT SEMINARS
7 May 2021
Speaker: Lynna Chu (Iowa State University)
Title: Sequential Change-point Detection for High-Dimensional and non-Euclidean Data
Abstract - In many modern applications, high-dimensional/non-Euclidean data sequences are collected to study complex phenomena over time and it is often of scientific significance to detect anomaly events as data is continually being collected. We study a nonparametric framework that utilizes nearest neighbor information among the observations and can be applied to various data types to detect changes in an online setting. We consider new test statistics under this framework that can detect anomaly events more effectively than the existing test with the false discovery rate controlled at the same level. Analytical formulas to determine the threshold of claiming a change are also provided, making the approach easily applicable for real data applications.
21 May 2021 - 3-4pm UK time
Speaker: Hao Ni (University College London)
Title and Abstract to Follow
4 June 2021
Speaker: Abolfazl Safikhani (University of Florida)
Title and Abstract to Follow
18 June 2021 - 3-4pm UK time
Speaker: Runmin Wang (Southern Methodist University)
Title and Abstract to Follow
2 July 2021 - 3-4pm UK time
Speaker: Kevin Lin (University of Pennsylvania)
Title and Abstract to Follow
To register your interest in accessing the StatScale Seminars, contact Dr Hyeyoung Maeng
PREVIOUS SEMINARS
26th March 2021 - Hao Chen (UC Davis)
A universal event detection framework for neuropixels data
12th March 2021 - Sumanta Basu (Cornell University)
Learning Financial Networks with Graphical Models of Time Series Data
26th February 2021 - Eric Kolaczyk (Boston University)
How hard is it to work with a network `average'?
12th February 2021 - Matteo Barigozzi (Università di Bologna)
Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm
29th January 2021 - Holger Dette (Ruhr-Universitaet Bochum)
Testing Relevant Hypotheses in Functional Time Series via Self-Normalization
4th December 2020 - Priyanga Dilini Talagala (University of Moratuwa)
Anomaly Detection in Streaming Time Series Data
20th November 2020 - Florian Pein (University of Cambridge)
About the loss function for cross-validation in change-point regression
6th November 2020 - Alex Aue (UC Davis)
Random matrix theory aids statistical inference in high dimensions
23rd October 2020 - Yoav Zemel (University of Cambridge)
Probabilistic approximations to optimal transport
9th October 2020 - Solt Kovacs (ETH Zurich)
17th July 2020 - Tobias Kley (University of Bristol)
A new approach for open-end sequential change point monitoring
3rd July, 2020 - Claudia Kirch (Otto-von-Guericke University)
Functional change point detection for fMRI data
19th June, 2020 - Martin Tveten (Dept. of Mathematics, University of Oslo)
Scalable changepoint and anomaly detection in cross-correlated data
5th June, 2020 - Yudong Chen (University of Cambridge)
High-dimensional, multiscale online changepoint detection