 A unifying approach to the shape and change-point hypotheses in the discrete univariate exponential familyChihiro Hirotsu, Shoichi Yamamoto and Harukazu Tsuruta Computational Statistics & Data Analysis, 2016, vol. 97, issue C, 33-46 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. Keywords: Cumulative sum statistic; Goodness-of-fit test; Markov property; Maximal contrast test; Recursion formula; Restricted alternative (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:97:y:2016:i:c:p:33-46 DOI: 10.1016/j.csda.2015.11.012 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.