Teaching

Duke University :: Department of Statistical Science

  • 2019 - 2020 Academic Year
    • STA 523 | Statistical Programming

Full course descriptions and course portals

Michigan State University :: Department of Statistics and Probability

  • 2018 - 2019 Academic Year
    • STT 191 | Introduction to Data Science
    • STT 301 | Computational Methods for Data Science
    • STT 315 | Introduction to Business Statistics (Online)

  • 2017 - 2018 Academic Year
    • STT 231 | Statistics for Scientists
    • STT 301 | Computational Methods for Data Science
    • STT 315 | Introduction to Business Statistics (Online)

  • 2016 - 2017 Academic Year
    • STT 315 | Introduction to Business Statistics (Online)

  • 2014 - 2015 Academic Year
    • STT 315 | Introduction to Business Statistics

  • 2013 - 2014 Academic Year
    • STT 200 | Statistical Methods

Full course descriptions and course portals

Publications

This paper deals with the detection and identification of changepoints among covariances of high-dimensional longitudinal data, where the number of features is greater than both the sample size and the number of repeated measurements. The proposed methods are applicable under general temporal-spatial dependence. A new test statistic is introduced for changepoint detection, and its asymptotic distribution is established. If a changepoint is detected, an estimate of the location is provided. The rate of convergence of the estimator is shown to depend on the data dimension, sample size, and signal-to-noise ratio. Binary segmentation is used to estimate the locations of possibly multiple changepoints, and the corresponding estimator is shown to be consistent under mild conditions. Simulation studies provide the empirical size and power of the proposed test and the accuracy of the changepoint estimator. An application to a time-course microarray dataset identifies gene sets with significant gene interaction changes over time.

Contact

  • shawn.santo@duke.edu
  • Duke University
    Department of Statistical Science
    214 Old Chemistry Building
    Durham, NC 27708