Symbolic Data Analysis: Conceptual Statistics and Data Mining (Wiley Series in Computational Statistics)
Centre and Range method for fitting a linear regression model to symbolic interval data
Computational Statistics & Data Analysis
Fitting a Least Absolute Deviation Regression Model on Interval-Valued Data
SBIA '08 Proceedings of the 19th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Constrained linear regression models for symbolic interval-valued variables
Computational Statistics & Data Analysis
Bivariate generalized linear model for interval-valued variables
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Far beyond the classical data models: symbolic data analysis
Statistical Analysis and Data Mining
Brief overview of symbolic data and analytic issues
Statistical Analysis and Data Mining
Univariate and multivariate linear regression methods to predict interval-valued features
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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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 © 2012 Wiley Periodicals, Inc.