TY - JOUR
T1 - Generalized fiducial methods for testing quantitative trait locus effects in genetic backcross studies
AU - Ren, Pengcheng
AU - Liu, Guanfu
AU - Pu, Xiaolong
AU - Li, Yan
N1 - Publisher Copyright:
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2022
Y1 - 2022
N2 - In this paper, we propose generalized fiducial methods and construct four generalized p-values to test the existence of quantitative trait locus effects under phenotype distributions from a location-scale family. Compared with the likelihood ratio test based on simulation studies, our methods perform better at controlling type I errors while retaining comparable power in cases with small or moderate sample sizes. The four generalized fiducial methods support varied scenarios: two of them are more aggressive and powerful, whereas the other two appear more conservative and robust. A real data example involving mouse blood pressure is used to illustrate our proposed methods.
AB - In this paper, we propose generalized fiducial methods and construct four generalized p-values to test the existence of quantitative trait locus effects under phenotype distributions from a location-scale family. Compared with the likelihood ratio test based on simulation studies, our methods perform better at controlling type I errors while retaining comparable power in cases with small or moderate sample sizes. The four generalized fiducial methods support varied scenarios: two of them are more aggressive and powerful, whereas the other two appear more conservative and robust. A real data example involving mouse blood pressure is used to illustrate our proposed methods.
KW - Generalized fiducial inference
KW - Gibbs algorithm
KW - likelihood ratio test
KW - mixture model
KW - quantitative trait locus
UR - https://www.scopus.com/pages/publications/85122101860
U2 - 10.1080/24754269.2021.1984636
DO - 10.1080/24754269.2021.1984636
M3 - 文章
AN - SCOPUS:85122101860
SN - 2475-4269
VL - 6
SP - 148
EP - 160
JO - Statistical Theory and Related Fields
JF - Statistical Theory and Related Fields
IS - 2
ER -