skip to main content


Search for: All records

Award ID contains: 1650913

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract

    Change‐point detection studies the problem of detecting the changes in the underlying distribution of the data stream as soon as possible after the change happens. Modern large‐scale, high‐dimensional, and complex streaming data call for computationally (memory) efficient sequential change‐point detection algorithms that are also statistically powerful. This gives rise to a computation versus statistical power trade‐off, an aspect less emphasized in the past in classic literature. This tutorial takes this new perspective and reviews several sequential change‐point detection procedures, ranging from classic sequential change‐point detection algorithms to more recent non‐parametric procedures that consider computation, memory efficiency, and model robustness in the algorithm design. Our survey also contains classic performance analysis, which provides useful techniques for analyzing new procedures.

    This article is categorized under:

    Statistical Models > Time Series Models

    Algorithms and Computational Methods > Algorithms

    Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data

     
    more » « less
  2. Free, publicly-accessible full text available January 2, 2025
  3. Free, publicly-accessible full text available September 1, 2024
  4. Free, publicly-accessible full text available April 3, 2024