We briefly review the majorization–minimization (MM) principle and elaborate on the closely
related notion of proximal distance algorithms, a generic approach for solving constrained …

The expectation–maximization (EM) algorithm is a well‐known iterative algorithm for finding
maximum likelihood estimates from incomplete data and is used in several statistical models …

Fixed-point algorithms are popular in statistics and data science due to their simplicity,
guaranteed convergence, and applicability to high-dimensional problems. Well-known …

Compositional data are common in many fields, both as outcomes and predictor variables.
The inventory of models for the case when both the outcome and predictor variables are …

We briefly review the majorization–minimization (MM) principle and elaborate on the closely
related notion of proximal distance algorithms, a generic approach for solving constrained …

Synapses change on multiple timescales, ranging from milliseconds to minutes, due to a
combination of both short-and long-term plasticity. Here we develop an extension of the …

Independent component analysis is commonly applied to functional magnetic resonance
imaging (fMRI) data to extract independent components (ICs) representing functional brain …

Abstract Accepted by: Aris Syntetos This study thoroughly investigates the efficiency of
advanced numerical extrapolation methods aimed at enhancing the convergence of vector …

This work considers Maximum Likelihood Estimation (MLE) of a Toeplitz structured
covariance matrix. In this regard, an equivalent reformulation of the MLE problem is …

Single-index models are becoming increasingly popular in many scientific applications as
they offer the advantages of flexibility in regression modeling as well as interpretable …