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Semiparametric Regression in Size‐Biased Sampling
Author(s) -
Chen Ying Qing
Publication year - 2010
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2009.01260.x
Subject(s) - covariate , statistics , sample size determination , mathematics , semiparametric regression , regression analysis , regression , simple random sample , sampling (signal processing) , outcome (game theory) , linear regression , econometrics , computer science , population , demography , mathematical economics , filter (signal processing) , sociology , computer vision
Summary 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.