论文检索

组成型协变量回归中的变量选择AI 解读

Motivated by research problems arising in the analysis of gut microbiome and metagenomic data, we consider variable selection and estimation in high-dimensional regression with compositional covariates. We propose an ℓ1 regularization method for the linear log-contrast model that respects the unique features of compositional data. We formulate the proposed procedure as a constrained convex optimization problem and introduce a coordinate descent method of multipliers for…

协变量调整的精度矩阵估计及其遗传基因组学应用AI 解读

Motivated by analysis of genetical genomics data, we introduce a sparse high-dimensional multivariate regression model for studying conditional independence relationships among a set of genes adjusting for possible genetic effects. The precision matrix in the model specifies a covariate-adjusted Gaussian graph, which presents the conditional dependence structure of gene expression after the confounding genetic effects on gene expression are taken into account. We present a…

全基因组关联研究中稀疏信号恢复的样本量与功效分析AI 解读

Genome-wide association studies have successfully identified hundreds of novel genetic variants associated with many complex human diseases. However, there is a lack of rigorous work on evaluating the statistical power for identifying these variants. In this paper, we consider sparse signal identification in genome-wide association studies and present two analytical frameworks for detailed analysis of the statistical power for detecting and identifying the disease-associated…

聚类失效时间线性回归的广义矩估计：引文更正AI 解读

Although Qu et al. (2000) was cited in § 1 of our paper, it should also have been mentioned as the source of the basis representation of the inverse correlation matrix following our equation (3). Moreover our Theorem 3 rests on arguments similar to those leading to Theorem 1 of Qu et al. (2000). We thank Dr Qu for pointing this out. ‘A group bridge approach for variable selection’ BY JIAN HUANG, SHUANGGE MA, HUILIANG XIE AND CUN-HUI ZHANG [email protected][email…

聚类失效时间数据线性回归的广义矩估计AI 解读

We propose a generalized method of moments approach to the accelerated failure time model with correlated survival data. We study the semiparametric rank estimator using martingale-based moments. We circumvent direct estimation of correlation parameters by concatenating the moments and minimizing a quadratic objective function. We establish the consistency and asymptotic normality of the parameter estimators, and derive the limiting distribution of the objective function. We…