Additive distortion measurement errors regression models with exponential calibration. Issue 14 (22nd September 2022)

• Record Type: Journal Article
• Authors: Zhu, Xuehu; Zhang, Jun; Yang, Yiping
• Is Part Of: Journal of statistical computation and simulation. Volume 92:Issue 14(2022)
• Page Start: 3020
• Page End: 3044
• Publication Date: 2022-09-22
• Subjects: Exponential calibration -- local linear smoothing -- additive distortion -- measurement errors; 62G05 -- 62G08 -- 62G20; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2022.2055028
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : In this paper, we used the newly proposed exponential calibration for the additive distortion measurement errors models, where neither the response variable nor the covariates can be directly observed but are distorted in additive fashions by an observed confounding variable. By using the exponential calibrated variables, three estimators of parameters and empirical likelihood-based confidence intervals are proposed, and we studied the asymptotic properties of the proposed estimators. For the hypothesis testing of model checking, an adaptive Neyman test statistic restricted is proposed. Simulation studies demonstrate the performance of the proposed estimators and the test statistic. A real example is analysed to illustrate its practical usage.