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董青 《纯粹数学与应用数学》2016,32(2):206-211
研究在非高斯噪声下的Lasso的高维统计分析,给出了在误差噪声满足二阶矩有限条件下,Lasso方法的高维界估计,推广了现有的关于Lasso的主要理论结果.所得结果具有一定的理论及应用价值. 相似文献
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Regression models provide prediction frameworks for multivariate mutual information analysis that uses information concepts when choosing covariates (also called features) that are important for analysis and prediction. We consider a high dimensional regression framework where the number of covariates (p) exceed the sample size (n). Recent work in high dimensional regression analysis has embraced an ensemble subspace approach that consists of selecting random subsets of covariates with fewer than p covariates, doing statistical analysis on each subset, and then merging the results from the subsets. We examine conditions under which penalty methods such as Lasso perform better when used in the ensemble approach by computing mean squared prediction errors for simulations and a real data example. Linear models with both random and fixed designs are considered. We examine two versions of penalty methods: one where the tuning parameter is selected by cross-validation; and one where the final predictor is a trimmed average of individual predictors corresponding to the members of a set of fixed tuning parameters. We find that the ensemble approach improves on penalty methods for several important real data and model scenarios. The improvement occurs when covariates are strongly associated with the response, when the complexity of the model is high. In such cases, the trimmed average version of ensemble Lasso is often the best predictor. 相似文献
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In this article, for Lasso penalized linear regression models in high-dimensional settings, we propose a modified cross-validation (CV) method for selecting the penalty parameter. The methodology is extended to other penalties, such as Elastic Net. We conduct extensive simulation studies and real data analysis to compare the performance of the modified CV method with other methods. It is shown that the popular K-fold CV method includes many noise variables in the selected model, while the modified CV works well in a wide range of coefficient and correlation settings. Supplementary materials containing the computer code are available online. 相似文献
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《Journal of computational and graphical statistics》2013,22(4):984-1006
The Lasso is a very well-known penalized regression model, which adds an L1 penalty with parameter λ1 on the coefficients to the squared error loss function. The Fused Lasso extends this model by also putting an L1 penalty with parameter λ2 on the difference of neighboring coefficients, assuming there is a natural ordering. In this article, we develop a path algorithm for solving the Fused Lasso Signal Approximator that computes the solutions for all values of λ1 and λ2. We also present an approximate algorithm that has considerable speed advantages for a moderate trade-off in accuracy. In the Online Supplement for this article, we provide proofs and further details for the methods developed in the article. 相似文献
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为了设计和评价爆炸网络的可靠性,研究了影响爆炸网络可靠性的主要因素与可靠性特征量之间的定量关系。通过正交试验和均匀试验方法,首先对23个可能的影响因素建立正交试验分析,筛选出7个影响爆速的主要因素;然后基于主要影响因素构造有效的均匀试验,对试验结果进行回归分析,得到主要因素的取值与爆速间的定量模型。 相似文献
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We consider the signal recovery through an unconstrained minimization in the framework of mutual incoherence property. A sufficient condition is provided to guarantee the stable recovery in the noisy case. Furthermore, oracle inequalities of both sparse signals and non-sparse signals are derived under the mutual incoherence condition in the case of Gaussian noises. Finally, we investigate the relationship between mutual incoherence property and robust null space property and find that robust null space property can be deduced from the mutual incoherence property. 相似文献
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以油砂中钠元素为研究对象,首次应用近红外光谱,结合Lasso(least absolute shrinkage and selection operator)建模方法,建立了油砂金属钠含量的近红外光谱定量校正模型,并与传统的PLS建模方法进行比较。结果表明,两种方法建立的油砂金属钠含量校正模型都具有很高的精度,预测性能方面略有差异。在实验验证集与预测集中,PLS与Lasso算法的相关系数分别是:Rv=0.878 8,Rp=0.857 9和Rv=0.887 4,Rp=0.860 0。实验验证了使用近红外光谱快速测定油砂金属钠含量的有效性,并分析了PLS与Lasso算法的适用范围。 相似文献