Composite Estimation for Single-Index Models with Responses Subject to Detection Limits

• Published in: Scandinavian journal of statistics Vol. 45; no. 3; pp. 444 - 464
• Main Authors: TANG, YANLIN, WANG, HUIXIA JUDY, LIANG, HUA
• Format: Journal Article
• Language: English
• Published: Oxford Wiley Publishing 01.09.2018; Blackwell Publishing Ltd
• Subjects: censored quantile regression; composite quantile estimator; Computer simulation; detection limit; Economic models; Indexing; informative subset estimation; Mathematical models; Parameter estimation; Powell's estimator; Regression analysis; Tobit model; Viruses
• ISSN: 0303-6898; 1467-9469
• DOI: 10.1111/sjos.12307

We propose a semiparametric estimator for single-index models with censored responses due to detection limits. In the presence of left censoring, the mean function cannot be identified without any parametric distributional assumptions, but the quantile function is still identifiable at upper quantile levels. To avoid parametric distributional assumption, we propose to fit censored quantile regression and combine information across quantile levels to estimate the unknown smooth link function and the index parameter. Under some regularity conditions, we show that the estimated link function achieves the non-parametric optimal convergence rate, and the estimated index parameter is asymptotically normal. The simulation study shows that the proposed estimator is competitive with the omniscient least squares estimator based on the latent uncensored responses for data with normal errors but much more efficient for heavy-tailed data under light and moderate censoring. The practical value of the proposed method is demonstrated through the analysis of a human immunodeficiency virus antibody data set.