结构化稀疏聚合

基于稀疏树聚合的时间序列回归

Sparse Tree-Based Aggregation for Time Series Regressions

提出StarTime方法，利用时间树分层排列滞后项，通过凸惩罚实现系数聚合与稀疏选择，降低高维时间序列回归的维度，提高估计精度。

结构稀疏分位数回归的一种有效算法

An efficient algorithm for structured sparse quantile regression

分位数回归结合促进结构化（或组）稀疏性的惩罚进行研究。在参数向量上使用混合$\ell_{1,\infty}$ -范数对传统的分位数回归问题施加结构稀疏性。一个算法推导出计算相应的最小化问题的分段线性解路径。最后给出了算法的Matlab实现，并对算法的应用进行了研究。

通过重采样和模型聚合进行广义向量自回归参数支撑集的稀疏估计

Sparse estimation of parameter support sets for generalized vector autoregressions by resampling and model aggregation

针对离散多元时间序列稀疏似然模型，提出基于重采样和聚合 LASSO 子样本支撑集的算法，以估计广义向量自回归模型参数支撑集，在网络恢复和生态网络估计中表现优越。

Tree-based Node Aggregation in Sparse Graphical Models

High-dimensional graphical models are often estimated using regularization that is aimed at reducing the number of edges in a network. In this work, we show how even simpler networks can be produced by aggregating the nodes of the graphical model. We develop a new convex regularized method, called the tree-aggregated graphical lasso or tag-lasso, that estimates graphical models that are both edge-sparse and node-aggregated. The aggregation is performed in a data-driven fashion by leveraging side information in the form of a tree that encodes node similarity and facilitates the interpretation o…

Low Rank and Structured Modeling of High-dimensional Vector Autoregressions

Network modeling of high-dimensional time series data is a key learning task due to its widespread use in a number of application areas, including macroeconomics, finance and neuroscience. While the problem of sparse modeling based on vector autoregressive models (VAR) has been investigated in depth in the literature, more complex network structures that involve low rank and group sparse components have received considerably less attention, despite their presence in data. Failure to account for low-rank structures results in spurious connectivity among the observed time series, which may lead …

结构化稀疏回归及其在HIV耐药性研究中的应用

Structured, sparse regression with application to HIV drug resistance

本文介绍了一种新的正向逐步回归方法。我们的修改发现解决方案的回归问题，所选择的预测出现在一个结构化的模式，相对于一个预定义的距离测量的候选预测。我们的方法的动机是预测HIV-1耐药性的蛋白质序列的问题。我们发现，我们的方法提高了耐药性的可解释性，同时产生与标准方法相当的预测准确性。我们还证明了我们的方法在模拟研究，并提出了一些理论结果和连接。