2014/12/20 by Kani Chen, Yuanyuan Lin, Chen, Kani +5
Computer Science · Mathematics · #Asymptotic distribution #Bayesian Methods and Mixture Models #Censoring (clinical trials) #Computer science #Covariate #Econometrics #Estimator #FOS: Computer and information sciences #Inference #Mathematics #Methodology (stat.ME) #Sampling (signal processing) #Sampling distribution #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics #stat.ME
paper · pdf · doi:10.48550/arxiv.1412.6608
26 pages
arxiv created 2014/12/20 · openalex publication_date 2014/12/20 · arxiv updated 2016/10/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Response-biased sampling, in which samples are drawn from a popula- tion according to the values of the response variable, is common in biomedical, epidemiological, economic and social studies. In particular, the complete obser- vations in data with censoring, truncation or missing covariates can be regarded as response-biased sampling under certain conditions. This paper proposes to use transformation models, known as the generalized accelerated failure time model in econometrics, for regression analysis with response-biased sampling. With unknown error distribution, the transformation models are broad enough to cover linear re- gression models, the Cox's model and the proportional odds model as special cases. To the best of our knowledge, except for the case-control logistic regression, there is no report in the literature that a prospective estimation approach can work for biased sampling without any modification. We prove that the maximum rank corre- lation estimation is valid for response-biased sampling and establish its consistency and asymptotic normality. Unlike the inverse probability methods, the proposed method of estimation does not involve the sampling probabilities, which are often difficult to obtain in practice. Without the need of estimating the unknown trans- formation function or the error distribution, the proposed method is numerically easy to implement with the Nelder-Mead simplex algorithm, which does not require convexity or continuity. We propose an inference procedure using random weight- ing to avoid the complication of density estimation when using the plug-in rule for variance estimation. Numerical studies with supportive evidence are presented. Applications are illustrated with the Forbes Global 2000 data and the Stanford heart transplant data.