TY - JOUR
T1 - Generalized fiducial methods for testing the homogeneity of a three-sample problem with a mixture structure
AU - Ren, Pengcheng
AU - Liu, Guanfu
AU - Pu, Xiaolong
N1 - Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2023
Y1 - 2023
N2 - Recently, the likelihood ratio (LR) test was proposed to test the homogeneity of a three-sample model with a mixture structure. Because of the presence of the mixture structure, the null limiting distribution of the LR test has a complicated supremum form, which leads to challenges in determining p-values. In addition, the LR test cannot control type-I errors well under small to moderate sample size. In this paper, we propose seven generalized fiducial methods to test the homogeneity of the three-sample model. Via simulation studies, we find that our methods perform significantly better than the LR test method in controlling the type-I errors under small to moderate sample size, while they have comparable powers in most cases. A halibut data example is used to illustrate the proposed methods.
AB - Recently, the likelihood ratio (LR) test was proposed to test the homogeneity of a three-sample model with a mixture structure. Because of the presence of the mixture structure, the null limiting distribution of the LR test has a complicated supremum form, which leads to challenges in determining p-values. In addition, the LR test cannot control type-I errors well under small to moderate sample size. In this paper, we propose seven generalized fiducial methods to test the homogeneity of the three-sample model. Via simulation studies, we find that our methods perform significantly better than the LR test method in controlling the type-I errors under small to moderate sample size, while they have comparable powers in most cases. A halibut data example is used to illustrate the proposed methods.
KW - 62F03
KW - Fiducial inference
KW - Gibbs algorithm
KW - generalized p-values
KW - mixture model
KW - three-sample
UR - https://www.scopus.com/pages/publications/85121766855
U2 - 10.1080/02664763.2021.2017414
DO - 10.1080/02664763.2021.2017414
M3 - 文章
AN - SCOPUS:85121766855
SN - 0266-4763
VL - 50
SP - 1094
EP - 1114
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
IS - 5
ER -