Imputation for semiparametric transformation models with biased-sampling data.

ABSTRACT: Widely recognized in many fields including economics, engineering, epidemiology, health sciences, technology and wildlife management, length-biased sampling generates biased and right-censored data but often provide the best information available for statistical inference. Different from traditional right-censored data, length-biased data have unique aspects resulting from their sampling procedures. We exploit these unique aspects and propose a general imputation-based estimation method for analyzing length-biased data under a class of flexible semiparametric transformation models. We present new computational algorithms that can jointly estimate the regression coefficients and the baseline function semiparametrically. The imputation-based method under the transformation model provides an

SUBMITTER: Liu H 

PROVIDER: S-EPMC3440536 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature