TY - JOUR
T1 - Reweighting estimators for the transformation models with length-biased sampling data and missing covariates
AU - Qiu, Zhiping
AU - Ma, Huijuan
AU - Shi, Jianhua
N1 - Publisher Copyright:
© 2020 Taylor & Francis Group, LLC.
PY - 2022
Y1 - 2022
N2 - Length-biased sampling data are commonly observed in cross-sectional surveys and epidemiological cohort studies. Due to study design or accident, some components of the covariate vector are often missing. This article considers the statistical inference for the transformation models with length-biased sampling data and missing covariates. The reweighting estimating procedures are proposed for the unknown regression parameters when the selection probability is known, estimated non parametrically, or estimated parametrically. The large sample properties of the resulting estimators are studied. Simulation studies are presented to demonstrate the utility and efficiency of the proposed methods.
AB - Length-biased sampling data are commonly observed in cross-sectional surveys and epidemiological cohort studies. Due to study design or accident, some components of the covariate vector are often missing. This article considers the statistical inference for the transformation models with length-biased sampling data and missing covariates. The reweighting estimating procedures are proposed for the unknown regression parameters when the selection probability is known, estimated non parametrically, or estimated parametrically. The large sample properties of the resulting estimators are studied. Simulation studies are presented to demonstrate the utility and efficiency of the proposed methods.
KW - Length-biased sampling
KW - missing covariate data
KW - transformation models
KW - weighted estimating equation
UR - https://www.scopus.com/pages/publications/85091100214
U2 - 10.1080/03610926.2020.1812653
DO - 10.1080/03610926.2020.1812653
M3 - 文章
AN - SCOPUS:85091100214
SN - 0361-0926
VL - 51
SP - 4252
EP - 4275
JO - Communications in Statistics - Theory and Methods
JF - Communications in Statistics - Theory and Methods
IS - 13
ER -