[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Registration::
Ethics Considerations::
Contact us::
Site Facilities::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
Indexing and Abstracting



 
..
Social Media

..
Licenses
Creative Commons License
This Journal is licensed under a Creative Commons Attribution NonCommercial 4.0
International License
(CC BY-NC 4.0).
 
..
Similarity Check Systems


..
:: Search published articles ::
Showing 1 results for ‎dimension Reduction Subspace‎

Azam Rastin, Mohammadreza Faridrohani,
Volume 13, Issue 2 (2-2020)
Abstract

‎The methodology of sufficient dimension reduction has offered an effective means to facilitate regression analysis of high-dimensional data‎. ‎When the response is censored‎, ‎most existing estimators cannot be applied‎, ‎or require some restrictive conditions‎. ‎In this article modification of sliced inverse‎, ‎regression-II have proposed for dimension reduction for non-linear censored regression data‎. ‎The proposed method requires no model specification‎, ‎it retains full regression information‎, ‎and it provides a usually small set of composite variables upon which subsequent model formulation and prediction can be based‎. ‎Finally‎, ‎the performance of the method is compared based on the simulation studies and some real data set include primary biliary cirrhosis data‎. ‎We also compare with the sliced inverse regression-I estimator‎.



Page 1 from 1     

مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

Persian site map - English site map - Created in 0.05 seconds with 33 queries by YEKTAWEB 4710