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A Bayesian approach for locating change points in a compound Poisson process with application to detecting DNA copy number variations

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  • Paul J. Plummer
  • Jie Chen

Abstract

This work examines the problem of locating changes in the distribution of a Compound Poisson Process where the variables being summed are iid normal and the number of variable follows the Poisson distribution. A Bayesian approach is developed to identify the location of significant changes in any of the parameters of the distribution, and a sliding window algorithm is used to identify multiple change points. These results can be applied in any field of study where an interest in locating changes not only in the parameter of a normally distributed data set but also in the rate of their occurrence. It has direct application to the study of DNA copy number variations in cancer research, where it is known that the distances between the genes can affect their intensity level.

Suggested Citation

  • Paul J. Plummer & Jie Chen, 2014. "A Bayesian approach for locating change points in a compound Poisson process with application to detecting DNA copy number variations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(2), pages 423-438, February.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:423-438
    DOI: 10.1080/02664763.2013.840272
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    1. Ashish Sen & S. Srivastava, 1975. "On tests for detecting change in mean when variance is unknown," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 27(1), pages 479-486, December.
    2. Jie Chen & Ayten Yiğiter & Kuang-Chao Chang, 2011. "A Bayesian approach to inference about a change point model with application to DNA copy number experimental data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1899-1913, September.
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