Is Seeing Believing? A Practitioner’s Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies by Kun Fan, Srijana Subedi, Gongshun Yang, Xi Lu, Jie Ren, Cen Wu

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Constrained Heat Kernel Graph Diffusion Convolution: A High-Dimensional Statistical Approximation via Information Theory by Zhuo-Chen He

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Square-root lasso under correlated regressors: Tight statistical analysis with a wireless communications application by Ayed M. Alrashdi, Masad A. Alrasheedi

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Model-X knockoffs in the replication crisis era: Reducing false discoveries and researcher bias in social science research by Jing Zhou, Sebastian Scherr

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Variable Selection and Parameter Estimation with the Atan Regularization Method by Yanxin Wang, Li Zhu

“…Variable selection is fundamental to high-dimensional statistical modeling. Many variable selection techniques may be implemented by penalized least squares using various penalty functions. …”
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An Analysis of Vectorised Automatic Differentiation for Statistical Applications by Chun Fung Kwok, Dan Zhu, Liana Jacobi

“…Our formulation is well-suited to high-dimensional statistical applications, where finite differences (FD) scale poorly due to the need to repeat computations for each input dimension, resulting in significant overhead, and is advantageous in simulation-intensive settings—such as Markov Chain Monte Carlo (MCMC)-based inference—where FD requires repeated sampling and multiple function evaluations, while AD can compute exact derivatives in a single pass, substantially reducing computational cost. …”
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Consistent Estimators of the Population Covariance Matrix and Its Reparameterizations by Chia-Hsuan Tsai, Ming-Tien Tsai

“…The novel estimator is used to establish that the optimal decomposite <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msubsup><mi>T</mi><mrow><mi>T</mi></mrow><mn>2</mn></msubsup></semantics></math></inline-formula>-test has been retained. A high-dimensional statistical hypothesis testing problem is used to carry out statistical inference for high-dimensional principal component analysis-related problems without the sparsity assumption. …”
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Towards precision medicine strategies using plasma proteomic profiling for suspected gallbladder cancer: A pilot study by Ghada Nouairia, Martin Cornillet, Hannes Jansson, Annika Bergquist, Ernesto Sparrelid

“…Preoperative plasma samples were analyzed using a 7,500 proteomics panel from SomaScan®. High-dimensional statistical methods including machine learning regularization, were used to analyze the data. …”
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Volatile organic compounds exposure associated with sarcopenia in US adults from NHANES 2011–2018 by Pangbo Wang, Pangbo Wang, Pangbo Wang, Wei Chen, Hongwei Fang, Liwei Xu, Jun Zhao, Jing Huang

“…We also employed Weighted Quantile Sum (WQS) regression model, a high-dimensional statistical approach used to evaluate the joint effects of multiple exposures, and Bayesian Kernel Machine regression (BKMR) model, a combination of Bayesian and statistical learning methods, to assess the mixture effects of mVOCs on sarcopenia risk. …”
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