Abstract
For simultaneous dimension reduction and variable selection for general regression models, including the multi-index model as a special case, we propose a penalized minimum average variance estimation method, combining the ideas of minimum average variance estimation in dimension reduction and regularization in variable selection. The resulting estimator can be found in a computationally efficient manner. Under mild conditions, the new method can consistently select all relevant predictors and has the oracle property. Simulations and a data example demonstrate the effectiveness and efficiency of the proposed method.
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.
