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从惩罚到过参数化：高维线性变量误差模型的隐式正则化AI 解读

Regularization methods are crucial for the analysis of high-dimensional data, with most methods adding a penalty term to the loss function explicitly. In this paper, we introduce an implicit regularization technique through over-parameterization and propose a calibrated penalty-free (CPF) estimation method for high-dimensional linear errors-in-variables models. This method calibrates the bias caused by measurement errors while avoiding the bias introduced by penalty terms.…

多层网络社区检测的矩整合与偏差调整谱方法AI 解读

To detect the global community structure of multi-layer networks, individual layers might not provide sufficient information. It is of vital importance to effectively complement the information from the network data. In this paper, under the framework of multi-layer stochastic block model, a Spectral method with Moments integration and Bias Adjustment (SpecMBA) is proposed for community detection. The key distinguishing feature of SpecMBA is the adaptive integration of both…

高维部分线性测量误差模型的校准均衡估计与双重选择AI 解读

In practice, measurement error data is frequently encountered and needs to be handled appropriately. As a result of additional bias induced by measurement error, many existing estimation methods fail to achieve satisfactory performances. This paper studies high-dimensional partially linear measurement error models. It proposes a calibrated equilibrium (CARE) estimation method to calibrate the bias caused by measurement error and overcomes the technical difficulty of the…

用于大规模投资组合配置的合成回归模型AI 解读

Portfolio allocation is an important topic in financial data analysis. In this article, based on the mean-variance optimization principle, we propose a synthetic regression model for construction of portfolio allocation, and an easy to implement approach to generate the synthetic sample for the model. Compared with the regression approach in existing literature for portfolio allocation, the proposed method of generating the synthetic sample provides more accurate…

RANK：基于图结构非线性 Knockoffs 的大规模推断AI 解读

Power and reproducibility are key to enabling refined scientific discoveries in contemporary big data applications with general high-dimensional nonlinear models. In this paper, we provide theoretical foundations on the power and robustness for the model-X knockoffs procedure introduced recently in Candès, Fan, Janson and Lv (2018) in high-dimensional setting when the covariate distribution is characterized by Gaussian graphical model. We establish that under mild regularity…

基于稳健秩相关的变量筛选AI 解读

Independence screening is a variable selection method that uses a ranking criterion to select significant variables, particularly for statistical models with nonpolynomial dimensionality or “large $p$, small $n$” paradigms when $p$ can be as large as an exponential of the sample size $n$. In this paper we propose a robust rank correlation screening (RRCS) method to deal with ultra-high dimensional data. The new procedure is based on the Kendall $\tau$ correlation coefficient…