Large datasets are increasingly common and are often difficult to interpret. Principal
component analysis (PCA) is a technique for reducing the dimensionality of such datasets …

This is the second edition of a monograph on generalized linear models with random effects
that extends the classic work of McCullagh and Nelder. It has been thoroughly updated, with …

Principal components (PCs) are widely used in statistics and refer to a relatively small
number of uncorrelated variables derived from an initial pool of variables, while explaining …

Sparse principal component analysis (SPCA) has emerged as a powerful technique for
modern data analysis, providing improved interpretation of low-rank structures by identifying …

Large-scale “omics” data have been increasingly used as an important resource for
prognostic prediction of diseases and detection of associated genes. However, there are …

Due to the rapid growth of smart agents such as weakly connected computational nodes and
sensors, developing decentralized algorithms that can perform computations on local agents …

The partial least-squares (PLS) method is designed for prediction problems where the
number of predictors is larger than the number of training samples. PLS is based on latent …

Abstract The Southern Ocean is a critical component of Earth's climate system, but its
remoteness makes it challenging to develop a holistic understanding of its processes from …

Background Ovarian cancer (OC) is distinguished by its aggressive nature and the limited
efficacy of current treatment strategies. Recent studies have emphasized the significant role …

Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful
in various fields by allowing random effects in regression models. Interest in the topic has …