Complex Data Analysis: modeling of interval-valued functional Data

Recent technological advances have led to the appearance of high-dimensional and complex datasets. Functional data analysis is one of the most commonly used techniques in modeling such complex datasets. This article introduces some functional methods to fit a functional regression model on the interval-valued functional data. Fourier basis system is considered for estimating the model parameters. In the first proposed method, a functional linear regressionmodel is fitted based on the midpoints of the intervals. The second method involves two independent functional linear models on the midpoint and the half range of the intervals. Furthermore, the third method is based on a combination of the midpoint and the half range of intervals. The applicability and advantages of the proposed models are investigated through a real data example