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Change-point analysis using logarithmic quantile estimation

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  • Tabacu, Lucia
  • Ledbetter, Mark

Abstract

We present a new approach for estimating quantiles for change-point problems, specifically for Pettitt’s rank test (1979). We use the logarithmic quantile estimation procedure introduced by Thangavelu (2005), which is based on the concept of the almost sure limit theorem. Numerical results for small data sets and simulated data are given.

Suggested Citation

  • Tabacu, Lucia & Ledbetter, Mark, 2019. "Change-point analysis using logarithmic quantile estimation," Statistics & Probability Letters, Elsevier, vol. 150(C), pages 94-100.
    • Handle: RePEc:eee:stapro:v:150:y:2019:i:c:p:94-100
    DOI: 10.1016/j.spl.2019.02.014
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    References listed on IDEAS

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    1. A. N. Pettitt, 1979. "A Non‐Parametric Approach to the Change‐Point Problem," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(2), pages 126-135, June.
    2. Holzmann, Hajo & Koch, Susanne & Min, Aleksey, 2004. "Almost sure limit theorems for U-statistics," Statistics & Probability Letters, Elsevier, vol. 69(3), pages 261-269, September.
    3. Lacey, Michael T. & Philipp, Walter, 1990. "A note on the almost sure central limit theorem," Statistics & Probability Letters, Elsevier, vol. 9(3), pages 201-205, March.
    4. Antoch, Jaromír & Husková, Marie, 2001. "Permutation tests in change point analysis," Statistics & Probability Letters, Elsevier, vol. 53(1), pages 37-46, May.
    5. Berkes, István & Csáki, Endre, 2001. "A universal result in almost sure central limit theory," Stochastic Processes and their Applications, Elsevier, vol. 94(1), pages 105-134, July.
    6. Gombay, Edit, 1994. "Testing for change-points with rank and sign statistics," Statistics & Probability Letters, Elsevier, vol. 20(1), pages 49-55, May.
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