On the nonparametric change-point detection for some complex data types

Abstract

In this presentation, we introduce general ideas for constructing change point tests in sequences of independent and identically distributed (i.i.d.) real-valued random variables. We illustrate these concepts with well-known examples and motivating applications to highlight their significance. Additionally, we show how these concepts can be extended to address complex data types, including matrix valued random variables and directional data. During the presentation, we highlight existing research directions and discuss potential paths for future research

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