IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v97y2016icp33-46.html
   My bibliography  Save this article

A unifying approach to the shape and change-point hypotheses in the discrete univariate exponential family

Author

Listed:
  • Hirotsu, Chihiro
  • Yamamoto, Shoichi
  • Tsuruta, Harukazu

Abstract

A unifying approach to the shape and change-point hypotheses is extended generally to a discrete univariate exponential family. The maximal contrast type tests are newly proposed for the convexity and sigmoidicity hypotheses based on the complete class lemma of tests for the restricted alternatives. Those tests are also efficient score tests for the slope change-point and inflection point models, respectively. For each of those tests the successive component statistics are the doubly- and triply-accumulated statistics. They have nice Markov properties for the exact and efficient recursion formulae for calculating the p-value. Further the sum of squares of the component statistics are developed as the cumulative chi-squared statistics for the directional goodness-of-fit tests of the dose–response model. Therefore the interesting applications will be in monitoring of spontaneous reporting of the adverse drug events and the directional goodness-of-fit tests.

Suggested Citation

  • Hirotsu, Chihiro & Yamamoto, Shoichi & Tsuruta, Harukazu, 2016. "A unifying approach to the shape and change-point hypotheses in the discrete univariate exponential family," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 33-46.
  • Handle: RePEc:eee:csdana:v:97:y:2016:i:c:p:33-46
    DOI: 10.1016/j.csda.2015.11.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947315002959
    Download Restriction: Full text for ScienceDirect subscribers only.

    File URL: https://libkey.io/10.1016/j.csda.2015.11.012?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. Hirotsu, C., 2009. "Clustering rows and/or columns of a two-way contingency table and a related distribution theory," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4508-4515, October.
    2. Chihiro Hirotsu & Eri Ohta & Nobuyoshi Hirose & Kenichiro Shimizu, 2003. "Profile Analysis of 24-Hours Measurements of Blood Pressure," Biometrics, The International Biometric Society, vol. 59(4), pages 907-915, December.
    3. Chihiro Hirotsu & Kohei Marumo, 2002. "Changepoint Analysis as a Method for Isotonic Inference," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(1), pages 125-138, March.
    4. Chihiro Hirotsu & Satoshi Aoki & Toshiya Inada & Yoshie Kitao, 2001. "An Exact Test for the Association Between the Disease and Alleles at Highly Polymorphic Loci with Particular Interest in the Haplotype Analysis," Biometrics, The International Biometric Society, vol. 57(3), pages 769-778, September.
    5. Hirotsu, Chihiro & Srivastava, Muni S., 2000. "Simultaneous confidence intervals based on one-sided max t test," Statistics & Probability Letters, Elsevier, vol. 49(1), pages 25-37, August.
    Full references (including those not matched with items on IDEAS)

    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. repec:jss:jstsof:23:i08 is not listed on IDEAS
    2. Chen, John T. & Hoppe, Fred M., 2004. "A connection between successive comparisons and ranking procedures," Statistics & Probability Letters, Elsevier, vol. 67(1), pages 19-25, March.
    3. Ueki, Masao, 2021. "Testing conditional mean through regression model sequence using Yanai’s generalized coefficient of determination," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    4. Chihiro Hirotsu & Eri Ohta & Nobuyoshi Hirose & Kenichiro Shimizu, 2003. "Profile Analysis of 24-Hours Measurements of Blood Pressure," Biometrics, The International Biometric Society, vol. 59(4), pages 907-915, December.
    5. Kin Yau Wong & Yair Goldberg & Jason P. Fine, 2016. "Oracle estimation of parametric models under boundary constraints," Biometrics, The International Biometric Society, vol. 72(4), pages 1173-1183, December.

    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:eee:csdana:v:97:y:2016:i:c:p:33-46. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.