Statistical Computation Papers of the Week

We propose CDaLCqr, a unified coordinate descent algorithm for penalized longitudinal log-contrast quantile regression accommodating popular penalties, such as adaptive Lasso, smoothly clipped absolute deviation (SCAD), and minimax concave penalty (MCP) within a single framework.

We further link path geometry to transport learning, showing that long annealing paths can slow population optimization, while sufficiently regular paths permit rapid convergence. Motivated by these findings, we propose componentwise annealed transport (CAT) to reduce the need for mass transfer between modes.

In this article we develop a new sequential entropic optimal transport filter (EOTF).

However, due to the combinatorial growth of the cycle space with graph size, the computational cost for such sampling is high. Therefore, we here introduce a sampling framework that enables us to sample from the cycle structure without enumerating all cycles.

We implement this method as a CPU-based extension to the BEAGLE library and evaluate it on two large-state molecular models: a Markov-modulated codon model of heterotachy with $M$ selective regimes and $61\cdot M$ possible states, and a codon-pair model of interlocus gene conversion between two paralogs with $61^2=3\,721$ possible states.

Applications to single-condition gut microbiota, paired leaf--root microbiota and three-visit human gut multi-omics illustrate the framework across different numbers of conditions.

We study direct subgradient methods for nonsmooth sample average approximation (SAA).

This paper introduces marp, an R package for model-averaged inference in parametric renewal processes.

This review summarizes the state of the art in methods that enable scalable risk and reliability analysis using multifidelity control variates, multifidelity importance sampling, and multifidelity active learning.

Exact Bayesian inference sequentially updates the posterior of whether and when a change has occurred. We present a unified approach to calculating and updating this posterior for a class of regression models covering standard changepoint scenarios, such as changes in mean and slope, whilst also allowing common structure such as seasonal effects.

We develop a unified framework linking each setting to a static optimization problem.

This work describes a computational approach for rapidly estimating and characterizing the covariance structure of a Gaussian random field from a modest number of i.i.d. replicates by formulating the problem in terms of a sparse inverse Cholesky factor of the data's covariance matrix.

As our main application, we analyze the preconditioned Crank-Nicolson (pCN) algorithm for sampling from measures on a Hilbert space that are absolutely continuous with respect to a Gaussian reference measure.

Bayesian Fusion performs distributed Bayesian inference exactly, but at a sizeable computational cost. We propose a method that uses numerical approximations to reduce the cost of the most computationally expensive steps of Bayesian Fusion, and expands its domain of applicability.

We propose a weighted extension of the Henze-Zirkler (HZ) test to assess bivariate normality of random effects, accounting for study-specific precision via inverse-variance weighting.

In this paper, we extend the results of Glasserman and Yao (1992) to demonstrate that common random number (CRN) simulation applied to coordinatewise monotone iterated random functions (IRF) represents the optimal allocation of random numbers to minimize the expected distance of an $n$-step Markov chain (MC) in $\mathbb{R}^q$.

Its fitting pipeline combines multidimensional scaling initialization with local refinement, global optimization when needed, and seeded restarts.

We introduce hypergraph cycle enumeration (HCE), a simple spectral framework for detecting and recovering planted signals in noisy hypergraph inference problems.

We describe the underlying models, package design, and the main functions for fitting, summarizing, and plotting posterior results.

We study Bayesian posterior sampling when the likelihood is an expectation that is costly to evaluate.