This paper presents the Lookup Table Regression Model (LTRM) for histogram-valued symbolic data. We first transform the given symbolic data to a numerical data table by the quantile method. Then, under the selected response variable, we apply the Mon...
In an era where large-scale data are produced and collected rapidly, great interest is attributed to symbolic data analysis in order to explore connotative and significant information from massive data. Recently, novel statistical techniques for hist...
In existing principle component analysis (PCA) methods for histogram-valued symbolic data, projection results are approximated based on Moore’s algebra and fail to reflect the data’s true structure, mainly because there is no precise, unified calcula...
This paper presents an unsupervised feature selection method for multi-dimensional histogram-valued data. We define a multi-role measure, called the compactness, based on the concept size of given objects and/or clusters described using a fixed numbe...
This study presents an extended vector polar histogram (EVPH) method for efficient robot navigation using omni-directional LiDAR data. Although the conventional vector polar histogram (VPH) method is a powerful technique suitable for LiDAR sensors, i...
In studies on the health impacts of air pollution, regression analysis continues to advance far beyond classical linear regression, which many scientists may have become familiar with in an introductory statistics course. With each new level of compl...
Entropy is re-examined as a quantification of ignorance in the predictability of a one dimensional continuous phenomenon. Although traditional estimators for entropy have been widely utilized in this context, we show that both the thermodynamic and S...