Abstract
We generalize the cumulative slicing estimator to dimension reduction where the predictors are subject to measurement errors. Unlike existing methodologies, our proposal involves neither nonparametric smoothing in estimation nor normality assumption on the predictors or measurement errors. We establish strong consistency and asymptotic normality of the resultant estimators, allowing that the predictor dimension diverges with the sample size. Comprehensive simulations have been carried out to evaluate the performance of our proposal and to compare it with existing methods. A dataset is analyzed to further illustrate the proposed methodology.
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.
