Abstract
Least absolute deviations (LAD) estimator for multiple regression models with doubly censored data is proposed and the asymptotic normality of the estimator is established. The proposed method doesn't require the censoring vectors to be identically distributed and the estimator can be applied to models with either fixed or random design. Moreover, as a generalization of the LAD estimator for models with complete observed data, the proposed estimator is robust to heteroscedasticity of the errors.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
