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Likelihood Ratio Tests for Interval Hypotheses with Applications

Author

Listed:
  • Junyong Park
  • Bimal Sinha
  • Arvind Shah
  • Dihua Xu
  • Jianxin Lin

Abstract

In this article, we derive the likelihood ratio tests (LRTs) for simultaneously testing interval hypotheses for normal means with known and unknown variances, and also with unknown but equal variance. Special cases when the interval hypotheses boil down to a point hypothesis are also discussed. Remarks regarding comparison of the LRT with tests based on combination of p-values are made, and several applications based on real data are mentioned.

Suggested Citation

  • Junyong Park & Bimal Sinha & Arvind Shah & Dihua Xu & Jianxin Lin, 2015. "Likelihood Ratio Tests for Interval Hypotheses with Applications," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(11), pages 2351-2370, June.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:11:p:2351-2370
    DOI: 10.1080/03610926.2013.781639
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    Cited by:

    1. Filipe J. Marques & Carlos A. Coelho & Paulo C. Rodrigues, 2017. "Testing the equality of several linear regression models," Computational Statistics, Springer, vol. 32(4), pages 1453-1480, December.
    2. Amanda Plunkett & Junyong Park, 2019. "Two-sample test for sparse high-dimensional multinomial distributions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 804-826, September.

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