Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics)
Symbolic Data Analysis: Conceptual Statistics and Data Mining (Wiley Series in Computational Statistics)
Testing linear independence in linear models with interval-valued data
Computational Statistics & Data Analysis
Centre and Range method for fitting a linear regression model to symbolic interval data
Computational Statistics & Data Analysis
Symbolic Data Analysis and the SODAS Software
Symbolic Data Analysis and the SODAS Software
Forecasting models for interval-valued time series
Neurocomputing
Techniques for evaluating fault prediction models
Empirical Software Engineering
Constrained linear regression models for symbolic interval-valued variables
Computational Statistics & Data Analysis
A robust method for linear regression of symbolic interval data
Pattern Recognition Letters
Far beyond the classical data models: symbolic data analysis
Statistical Analysis and Data Mining
Logistic regression-based pattern classifiers for symbolic interval data
Pattern Analysis & Applications
Robust regression with application to symbolic interval data
Engineering Applications of Artificial Intelligence
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Kernel regression is more attractive when it is not possible to determine explicit parametric form of the model and moreover, it does not depend on probabilistic distribution. This paper introduces kernel regression in which the input data set is described by interval-value variables. Two model families are considered. The first family estimates the bounds of the intervals regarding either a smooth function for center variables of the intervals (first model) or two smooth functions for range and center variables, respectively (second model). The second family performs the estimates of the intervals based on regression mixtures. These mixtures assume either a smooth function for center variables and a linear function based on least squares for range variables (third model) or a smooth function for range variables and a linear function for center variables (fourth model). The predictions of the lower and upper bounds of new intervals are computed and two different simulation studies are carried out to validate these predictions. Five real-life interval data sets are also considered. The prediction quality is assessed by a mean magnitude of relative error calculated from a test data set.