Leveraging independence in high-dimensional mixed linear regression.

We address the challenge of estimating regression coefficients and selecting relevant predictors in the context of mixed linear regression in high dimensions, where the number of predictors greatly exceeds the sample size. Recent advancements in this field have centered on incorporating sparsity-inducing penalties into the expectation-maximization (EM) algorithm, which seeks to maximize the conditional likelihood of the response given the predictors. However, existing procedures often treat predictors as fixed or overlook their inherent variability.

Bayesian regression analysis of skewed tensor responses.

Tensor regression analysis is finding vast emerging applications in a variety of clinical settings, including neuroimaging, genomics, and dental medicine. The motivation for this paper is a study of periodontal disease (PD) with an order-3 tensor response: multiple biomarkers measured at prespecified tooth-sites within each tooth, for each participant. A careful investigation would reveal considerable skewness in the responses, in addition to response missingness.

The province-managing-county reform and green innovation of enterprises: evidence from the quasi-natural experiment.

Based on the database of green patents of China's A-share listed enterprises from 2001 to 2018, this paper identifies the impact of the province-managing-county (PMC) fiscal reform on the green innovation performance of enterprises by using staggered DID method. The results show that the PMC fiscal reform significantly promotes enterprises' green innovation performance, and this impact is not only reflected in the quantity of green patents but also in the quality. The mechanism test finds that the policy effect of PMC fiscal reform comes from the attraction of environmental investment and optimization of human capital structure.

An iterative penalized least squares approach to sparse canonical correlation analysis.

It is increasingly interesting to model the relationship between two sets of high-dimensional measurements with potentially high correlations. Canonical correlation analysis (CCA) is a classical tool that explores the dependency of two multivariate random variables and extracts canonical pairs of highly correlated linear combinations. Driven by applications in genomics, text mining, and imaging research, among others, many recent studies generalize CCA to high-dimensional settings.

MicroRNA-210 is increased and it is required for dedifferentiation of osteosarcoma cell line.

Osteosarcoma (OS) is the most common malignant bone tumor and is prevalent in adolescents. In clinical studies, miR-210 has been reported to be tightly correlated to the poor prognosis of OS. Nevertheless, its roles in OS have not been fully elucidated.