Semiparametric regression in size-biased sampling.
Size-biased sampling arises when a positive-valued outcome variable is sampled with selection probability proportional to its size. In this article, we propose a semiparametric linear regression model to analyze size-biased outcomes. In our proposed model, the regression parameters of covariates are of major interest, while the distribution of random errors is unspecified. Under the proposed model, we discover that regression parameters are invariant regardless of size-biased sampling. Following this invariance property, we develop a simple estimation procedure for inferences. Our proposed methods are evaluated in simulation studies and applied to two real data analyses.
Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA. yqchen@scharp.org
Ying Qing
YQ
Chen
D000465:Algorithms; D015982:Bias; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D012044:Regression Analysis; D018401:Sample Size