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A resampling approach for interval‐valued data regression
Author(s) -
Ahn Jeongyoun,
Peng Muliang,
Park Cheolwoo,
Jeon Yongho
Publication year - 2012
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11150
Subject(s) - resampling , computer science , data mining , interval (graph theory) , linear regression , regression analysis , regression , data modeling , statistical hypothesis testing , statistics , statistical inference , artificial intelligence , machine learning , mathematics , combinatorics , database
We consider interval‐valued data that frequently appear with advanced technologies in current data collection processes. Interval‐valued data refer to the data that are observed as ranges instead of single values. In the last decade, several approaches to the regression analysis of interval‐valued data have been introduced, but little work has been done on relevant statistical inferences concerning the regression model. In this paper, we propose a new approach to fit a linear regression model to interval‐valued data using a resampling idea. A key advantage is that it enables one to make inferences on the model such as the overall model significance test and individual coefficient test. We demonstrate the proposed approach using simulated and real data examples, and also compare its performance with those of existing methods. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012