基于LASSO回归的R-vine copula模型构建及其在化工过程故障检测中的应用

Vine copula模型在描述高维数据间的非线性、非高斯特性相依关系问题上提供了一种新的思路，在化工过程建模领域受到越来越多关注。笔者将LASSO(least absolute shrinkage and selection operator)回归引入R-vine copula(LASSO-R-vine copula, LRVC),根据变量间联系的强弱程度确定变量在R-vine矩阵中的位置，利用回归分析正则化路径选择R-vine copula矩阵结构，遵循R-vine矩阵构建规则和回归过程确定R-vine结构矩阵模型，以获得一个与变量独立性有关的稀疏矩阵模型。该方法构建的矩阵结构独立于copula函数类型和参数，在处理高维度复杂工业过程数据时，利用稀疏模型和惩罚力度简化copula函数类型选择过程，缩短了建模时间，使统计建模具有更强的灵活性。该方法在TE(Tennessee Eastman)和醋酸脱水过程故障监测中表现出较好的预测效果，证明了提出的方法在非线性、非高斯过程的有效性。     

农杆菌介导禾本科牧草遗传转化的研究进展

建立禾本科牧草的遗传转化体系对于种质资源利用具有重要意义.近年来,假俭草(Eremochloa ophiuroides)、黑麦草(Lolium perenne)、结缕草(Zoysia japonica)、柳枝稷(Panicum virgatum)、高羊茅(Festuca elata)、二穗短柄草(Brachypodiu...     

Large Dynamic Covariance Matrix Estimation with an Application to Portfolio Allocation: A Semiparametric Reproducing Kernel Hilbert Space Approach

The estimation of high dimensional covariance matrices is an interesting and important research topic for many empirical time series problems such as asset allocation. To solve this dimension dilemma, a factor structure has often been taken into account. This paper proposes a dynamic factor structure whose factor loadings are generated in reproducing kernel Hilbert space (RKHS), to capture the dynamic feature of the covariance matrix. A simulation study is carried out to demonstrate its performance. Four different conditional variance models are considered for checking the robustness of our method and solving the conditional heteroscedasticity in the empirical study. By exploring the performance among eight introduced model candidates and the market baseline, the empirical study from 2001 to 2017 shows that portfolio allocation based on this dynamic factor structure can significantly reduce the variance, i.e., the risk, of portfolio and thus outperform the market baseline and the ones based on the traditional factor model.