Two‐sample testing for random graphs
The employment of two‐sample hypothesis testing in examining random graphs has been a prevalent approach in diverse fields such as social sciences, neuroscience, and genetics. We advance a spectral‐based two‐sample hypothesis testing methodology ...
Neural interval‐censored survival regression with feature selection
Survival analysis is a fundamental area of focus in biomedical research, particularly in the context of personalized medicine. This prominence is due to the increasing prevalence of large and high‐dimensional datasets, such as omics and medical ...
A random forest approach for interval selection in functional regression
In this article, we focus on the problem of variable selection in a functional regression framework. This question is motivated by practical applications in the field of agronomy: In this field, identifying the temporal periods during which ...
Characterizing climate pathways using feature importance on echo state networks
The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the ...
Revisiting Winnow: A modified online feature selection algorithm for efficient binary classification
Winnow is an efficient binary classification algorithm that effectively learns from data even in the presence of a large number of irrelevant attributes. It is specifically designed for online learning scenarios. Unlike the Perceptron algorithm, ...
A new logarithmic multiplicative distortion for correlation analysis
We study the Pearson correlation coefficient in a logarithmic manner under the presence of multiplicative distortion measurement errors. In this context, the observed variables with logarithmic transformation are distorted in multiplicative ...
