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Motivation: Differential network inference is a fundamental and challenging problem to reveal gene interactions and regulation relationships under different conditions. Many algorithms have been devel...
分析省级层面就医需求的政策变量和交互要素,并控制地区和时间效应的异质性,为精确估计医疗改革效应和医疗机构区域合理布局提供科学依据.以就医需求和就医供给的代理变量、区域特征控制变量建立指标体系,采用Post-double-selection-LASSO方法选择潜在变量及其函数形式.一阶差分、全控制变量和各省标准差集聚三个模型的比较结果显示,标准差集聚模型较好地控制时间趋势和初始差异,证实复杂就医需求...
本征音子说话人自适应方法在自适应数据量不足时会出现严重的过拟合现象,提出了一种基于稀疏组 LASSO 约束的本征音子说话人自适应算法。首先给出隐马尔可夫—高斯混合模型下本征音子说话人自适应的基 本原理;然后将稀疏组LASSO 正则化引入到本征音子说话人自适应,通过调整权重因子控制模型的复杂度,并 通过一种加速近点梯度的数学优化算法来实现;最后将稀疏组LASSO 约束的自适应算法与当前多种正则...
We consider the problem of learning a coefficient vector x0 ∈ RN from noisy linear observation y = Ax0 + w ∈ Rn. In many contexts (ranging from model selection to image processing) it is desirable to...
We study the effective degrees of freedom of the lasso in the framework of Stein’s unbiased risk estimation (SURE). We show that the number of nonzero coefficients is an unbiased estimate for the degr...
We consider the least angle regression and forward stagewise algorithms for solving penalized least squares regression problems. In Efron,Hastie, Johnstone & Tibshirani (2004) it is proved that the le...
We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm— the ...
We consider the group lasso penalty for the linear model. We note that the standard algorithm for solving the problem assumes that the model matrices in each group are orthonormal. Here we consider a ...
In ordinary regression, imposition of a lasso penalty makes continuous model selection straightforward. Lasso penalized regression is particularly advantageous when the number of predictors far exceed...
We consider rules for discarding predictors in lasso regression and related problems, for computational efficiency. El Ghaoui et al. (2010) propose “SAFE” rules, based on univariate inner products bet...
Array-based comparative genomic hybridization (aCGH) enables the measurement of DNA copy number across thousands of locations in a genome. The main goals of analyzing aCGH data are to identify the reg...
The graphical lasso [5] is an algorithm for learning the structure in an undirected Gaussian graphical model, using ℓ1 regularization to control the number of zeros in the precision matrix Θ = Σ...
We propose several methods for estimating edge-sparse and nodesparse graphical models based on lasso and grouped lasso penalties.We develop efficient algorithms for fitting these models when the numbe...
We consider the sparse inverse covariance regularization problem or graphical lasso with regularization parameter λ. Suppose the sample covariance graph formed by thresholding the entries of the sampl...
We consider the problem of learning a coecient vector x0 2 R N from noisy linear observation y = Ax0 + w 2 R n. In many contexts (ranging from model selection to image processing) it is desirable to ...

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