IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v39y2012i5p947-961.html
   My bibliography  Save this article

Empirical likelihood ratio test for a mean change point model with a linear trend followed by an abrupt change

Author

Listed:
  • Wei Ning

Abstract

In this paper, a change point model with the mean being constant up to some unknown point, and increasing linearly to another unknown point, then dropping back to the original level is studied. A nonparametric method based on the empirical likelihood test is proposed to detect and estimate the locations of change points. Under some mild conditions, the asymptotic null distribution of an empirical likelihood ratio test statistic is shown to have the extreme distribution. The consistency of the test is also proved. Simulations of the powers of the test indicate that it performs well under different assumptions of the data distribution. The test is applied to the aircraft arrival time data set and the Stanford heart transplant data set.

Suggested Citation

  • Wei Ning, 2012. "Empirical likelihood ratio test for a mean change point model with a linear trend followed by an abrupt change," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(5), pages 947-961, September.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:947-961
    DOI: 10.1080/02664763.2011.628647
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2011.628647
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2011.628647?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Chen, Yu-Wang & Lu, Yong-Zai & Chen, Peng, 2007. "Optimization with extremal dynamics for the traveling salesman problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(1), pages 115-123.
    3. Zou, Changliang & Liu, Yukun & Qin, Peng & Wang, Zhaojun, 2007. "Empirical likelihood ratio test for the change-point problem," Statistics & Probability Letters, Elsevier, vol. 77(4), pages 374-382, February.
    4. Zhong Guan, 2004. "A semiparametric changepoint model," Biometrika, Biometrika Trust, vol. 91(4), pages 849-862, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cai, Xia & Tian, Yubin & Ning, Wei, 2019. "Change-point analysis of the failure mechanisms based on accelerated life tests," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 515-522.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Venkata Jandhyala & Stergios Fotopoulos & Ian MacNeill & Pengyu Liu, 2013. "Inference for single and multiple change-points in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 423-446, July.
    2. Narayanaswamy Balakrishnan & Laurent Bordes & Christian Paroissin & Jean-Christophe Turlot, 2016. "Single change-point detection methods for small lifetime samples," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(5), pages 531-551, July.
    3. Gabriela Ciuperca & Zahraa Salloum, 2015. "Empirical likelihood test in a posteriori change-point nonlinear model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(8), pages 919-952, November.
    4. Bill Russell & Dooruj Rambaccussing, 2019. "Breaks and the statistical process of inflation: the case of estimating the ‘modern’ long-run Phillips curve," Empirical Economics, Springer, vol. 56(5), pages 1455-1475, May.
    5. Joseph Ngatchou-Wandji & Echarif Elharfaoui & Michel Harel, 2022. "On change-points tests based on two-samples U-Statistics for weakly dependent observations," Statistical Papers, Springer, vol. 63(1), pages 287-316, February.
    6. 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.
    7. Zou, Changliang & Liu, Yukun & Qin, Peng & Wang, Zhaojun, 2007. "Empirical likelihood ratio test for the change-point problem," Statistics & Probability Letters, Elsevier, vol. 77(4), pages 374-382, February.
    8. Brennen T. Fagan & Marina I. Knight & Niall J. MacKay & A. Jamie Wood, 2020. "Change point analysis of historical battle deaths," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 909-933, June.
    9. Li, Boyan & Diao, Xundi, 2023. "Structural break in different stock index markets in China," The North American Journal of Economics and Finance, Elsevier, vol. 65(C).
    10. Fuqi Chen & Rogemar Mamon & Sévérien Nkurunziza, 2018. "Inference for a change-point problem under a generalised Ornstein–Uhlenbeck setting," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(4), pages 807-853, August.
    11. Sandip Sinharay, 2017. "Some Remarks on Applications of Tests for Detecting A Change Point to Psychometric Problems," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1149-1161, December.
    12. Minya Xu & Ping-Shou Zhong & Wei Wang, 2016. "Detecting Variance Change-Points for Blocked Time Series and Dependent Panel Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 213-226, April.
    13. Zhu, Xiaoqian & Xie, Yongjia & Li, Jianping & Wu, Dengsheng, 2015. "Change point detection for subprime crisis in American banking: From the perspective of risk dependence," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 18-28.
    14. Jon Vilasuso, 1996. "Changes in the duration of economic expansions and contractions in the United States," Applied Economics Letters, Taylor & Francis Journals, vol. 3(12), pages 803-806.
    15. Philip Preuss & Ruprecht Puchstein & Holger Dette, 2015. "Detection of Multiple Structural Breaks in Multivariate Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 654-668, June.
    16. Watson, G. S., 1995. "Detecting a change in the intercept in multiple regression," Statistics & Probability Letters, Elsevier, vol. 23(1), pages 69-72, April.
    17. Cai, Xia & Tian, Yubin & Ning, Wei, 2019. "Change-point analysis of the failure mechanisms based on accelerated life tests," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 515-522.
    18. Bian, Zilin & Zuo, Fan & Gao, Jingqin & Chen, Yanyan & Pavuluri Venkata, Sai Sarath Chandra & Duran Bernardes, Suzana & Ozbay, Kaan & Ban, Xuegang (Jeff) & Wang, Jingxing, 2021. "Time lag effects of COVID-19 policies on transportation systems: A comparative study of New York City and Seattle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 145(C), pages 269-283.
    19. Xia Cai & Khamis Khalid Said & Wei Ning, 2016. "Change-point analysis with bathtub shape for the exponential distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(15), pages 2740-2750, November.
    20. Jean-François Quessy, 2019. "Consistent nonparametric tests for detecting gradual changes in the marginals and the copula of multivariate time series," Statistical Papers, Springer, vol. 60(3), pages 717-746, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:947-961. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.