5 papers · 1 filter
Scale two-sample testing with arbitrarily missing data
Yijin Zeng, Niall M. Adams, Dean A. Bodenham
This work proposes a novel rank-based scale two-sample testing method for univariate, distinct data when a subset of the data may be missing. Our approach is based on mathematicall…
Exact Bounds of Spearman's footrule in the Presence of Missing Data with Applications to Independence Testing
Yijin Zeng, Niall M. Adams, Dean A. Bodenham
This work studies exact bounds of Spearman's footrule between two partially observed -dimensional distinct real-valued vectors and . The lower bound is obtained by sequen…
Online Changepoint Detection via Dynamic Mode Decomposition
Victor K. Khamesi, Niall M. Adams, Dean A. Bodenham +1
Detecting changes in data streams is a vital task in many applications. There is increasing interest in changepoint detection in the online setting, to enable real-time monitoring…
MMD Two-sample Testing in the Presence of Arbitrarily Missing Data
Yijin Zeng, Niall M. Adams, Dean A. Bodenham
In many real-world applications, it is common that a proportion of the data may be missing or only partially observed. We develop a novel two-sample testing method based on the Max…
On two-sample testing for data with arbitrarily missing values
Yijin Zeng, Niall M. Adams, Dean A. Bodenham
We develop a new rank-based approach for univariate two-sample testing in the presence of missing data which makes no assumptions about the missingness mechanism. This approach is…