摘要翻译：
当必须估计一系列（相关的）线性模型时，通常适当地将不同的数据集组合起来以构造更有效的估计量。我们使用像Lasso或自适应Lasso这样的$\ell_1$projected估计器，它们可以同时进行参数估计和模型选择。我们证明了对于高维线性模型的一个时程，通过适当地组合不同的时间点，可以提高Lasso和自适应Lasso的收敛速度。此外，自适应套索仍然享有oracle属性和一致的变量选择。在模拟数据和DNA序列基序查找的实际问题上，说明了所提方法的有限样本性质。
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英文标题：
《Smoothing $\ell_1$-penalized estimators for high-dimensional time-course
  data》
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作者：
Lukas Meier, Peter B\"uhlmann
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最新提交年份：
2007
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分类信息：

一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  When a series of (related) linear models has to be estimated it is often appropriate to combine the different data-sets to construct more efficient estimators. We use $\ell_1$-penalized estimators like the Lasso or the Adaptive Lasso which can simultaneously do parameter estimation and model selection. We show that for a time-course of high-dimensional linear models the convergence rates of the Lasso and of the Adaptive Lasso can be improved by combining the different time-points in a suitable way. Moreover, the Adaptive Lasso still enjoys oracle properties and consistent variable selection. The finite sample properties of the proposed methods are illustrated on simulated data and on a real problem of motif finding in DNA sequences.
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PDF链接：
https://arxiv.org/pdf/712.1654