Semiparametric regression in size-biased sampling.

Publication Type:

Journal Article

Authors:

Chen, Ying Q

Source:

Biometrics, Volume 66, Issue 1, p.149-58 (2010)

Keywords:

2010, Algorithms, Bias (Epidemiology), Biometry, Center-Authored Paper, Computer Simulation, Data Interpretation, Statistical, Models, Statistical, Regression Analysis, sample size, Vaccine and Infectious Disease Division

Abstract:

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.