Phi-divergence statistics for the likelihood ratio order: an approach based on log-linear models Articles uri icon

authors

  • MARTIN APAOLAZA, NIRIAN
  • Mata, Raquel
  • PARDO, LEANDRO

publication date

  • September 2014

start page

  • 387

end page

  • 408

volume

  • 130

international standard serial number (ISSN)

  • 0047-259X

electronic international standard serial number (EISSN)

  • 1095-7243

abstract

  • When some treatments are ordered according to the categories of an ordinal categorical variable (e.g., extent of side effects) in a monotone order, one might be interested in knowing whether the treatments are equally effective or not. One way to do that is to test if the likelihood ratio order is strictly verified. A method based on log-linear models is derived to make statistical inference and phi-divergence test-statistics are proposed for the test of interest. Focused on log-linear modeling, the theory associated with the asymptotic distribution of the phi-divergence test-statistics is developed. An illustrative example motivates the procedure and a simulation study for small and moderate sample sizes shows that it is possible to find phi-divergence test-statistic with an exact size closer to nominal size and higher power in comparison with the classical likelihood ratio. (C) 2014 Elsevier Inc. All rights reserved.

keywords

  • phi-divergence test statistics; inequality constrains; likelihood ratio ordering; log-linear modeling; inference