Sparse principal component analysis (PCA) aims to find principal components as linear combinations of a subset of the original input variables without sacrificing the fidelity of the classical PCA.…
Uncertainty quantification (UQ) in machine learning is currently drawing increasing research interest, driven by the rapid deployment of deep neural networks across different fields, such as comput…
High-dimensional regression and regression with a left-censored response are each well-studied topics. In spite of this, few methods have been proposed which deal with both of these complications s…
In statistical analysis, researchers often perform coordinatewise Gaussianization such that each variable is marginally normal. The normal score transformation is a method for coordinatewise Gaussi…
Free-space optical (FSO) communication links can benefit from recovering both amplitude- and phase-encoded data (e.g., quadrature-amplitude-modulation, QAM) by mixing a local oscillator (LO) with a…
Bandwidth limitation introduced inter-symbol interference (ISI) is the major impediment for high baud rate intensity modulation and direct detection (IM/DD) system with commercial devices. Least me…
Nonlinear equalization (NE) plays a pivotal role in the analog intermediate-frequency over fiber (IFoF) mobile fronthaul (MFH) system demanding a high signal fidelity. This work proposes and experi…
In multiple change-point analysis, one of the main difficulties is to determine the number of change-points. Various consistent selection methods, including the use of Schwarz information criterion…
There is a vast amount of work on high-dimensional regression. The common starting point for the existing theoretical work is to assume the data generating model is a homoscedastic linear regressio…
The radio over fiber (RoF) system using optical heterodyne (OH) for broadband up-conversion and envelope detection (ED) for phase-noise-insensitive down-conversion stands out as a simple, robust, a…