Estimation of covariance matrix via the sparse Cholesky factor with lasso

In this paper, we discuss a parsimonious approach to estimation of high-dimensional covariance matrices via the modified Cholesky decomposition with lasso. Two different methods are proposed. They are the equi-angular and equi-sparse methods. We use simulation to compare the performance of the proposed methods with others available in the literature, including the sample covariance matrix, the banding method, and the L₁-penalized normal loglikelihood method. We then apply the proposed methods to a portfolio selection problem using 80 series of daily stock returns. To facilitate the use of lasso in high-dimensional time series analysis, we develop the dynamic weighted lasso (DWL) algorithm that extends the LARS-lasso algorithm. In particular, the proposed algorithm can efficiently update the lasso solution as new data become available. It can also add or remove explanatory variables. The entire solution path of the L₁-penalized normal loglikelihood method is also constructed.     

设计部门如何控制工程造价

当前我国的基本建设和建筑业的预算超概算 .概算超估算的现象十分严重 ,要避免这一现象的发生 ,必须加强设计控制     

包含测度的椭圆方程解的最大模的估计

对包含测度的椭圆方程 ,证明解的有界性已颇为困难 ,对解的最大模作出估计尤其困难。本文只对其中一种特殊情形作出解最大模的先验估计。     

Fisher information in randomly sampled sib pairs and extremely discordant sib pairs in genetic analysis for a quantitative trait locus

The conditional mixture likelihood method using the absolute difference of the trait values of a sib pair to estimate genetic parameters underlies commonly used method in linkage analysis. Here, the statistical properties of the model are examined. The marginal model with a pseudo-likelihood function based on a sample of the absolute difference of sib-traits is also studied. Both approaches are compared numerically. When genotyping is much more expensive than screening a quantitative trait, it is known that extremely discordant sib pairs provide more powerful linkage tests than randomly sampled sib pairs. The Fisher information about genetic parameters contained in extremely discordant sib pairs is calculated using the marginal mixture model. Our results supplement current research showing that extremely discordant sib pairs are powerful for the linkage detection by demonstrating they also contain more information about other genetic parameters.     

GM(1,N)模型的病态矩阵的产生及主成分估计改进法

本文在文献[1]的基础上给出了更为一般的探讨,并且用主成分估计法改进了灰色系统模型。     

最佳线性无偏估计中的一个影响度量

本文讨论数据的剔除对最佳线性无偏估计的影响大小。用方差比做为判定影响大小的度量,并找到了它与相关系数之间的精确关系。     

学习曲线在建筑施工工期预测中的应用研究

学习曲线体现的是累计生产量和单位平均生产时间之间的关系 ,经常用于企业生产中生产效率提高及成本降低等方面。根据某施工企业工程实际生产情况 ,利用一元线性回归分析法建立了学习曲线方程 ,以此估算人工工日及预测工期 ,并结合人工工日单价 ,估算人工费和确定投标报价 ,同时可应用学习曲线制定企业定额 ,进行项目成本核算。进一步探讨了应用学习曲线时应考虑的因素     

A Note on Ergodic Processes Prediction via Estimation of the Conditional Mode Function

We establish the uniform almost-sure convergence of a kernel estimate of the conditional density for an ergodic process. A useful application to the prediction of the ergodic process via the conditional mode function is also given.     

几何分布产品不完全数据场合下的统计分析

几何分布是离散型寿命分布中最为重要的分布之一,许多产品的寿命（比如开关等）都可以用几何分布来描述。由于几何分布的无记忆性,它在可靠性理论与应用概率模型中有着非常重要的地位。目前,对关于几何分布在全样本场合、截尾样本场合以及加速寿命试验场合下参数的统计分析已经有了广泛的研究。并且有着重要的理论与应用价值。因此将不完全数据场合下的几何分布问题转化为指数分布问题,再利用指数分布的已有结果首次得到了几何分布在缺失数据场合和分组数据场合下参数的近似点估计,Monte—Carlo模拟算例结果令人满意,说明该方法是可行的。     

Estimating the Probability of Rare Events Occurring Using a Local Model Averaging

In statistical applications, logistic regression is a popular method for analyzing binary data accompanied by explanatory variables. But when one of the two outcomes is rare, the estimation of model parameters has been shown to be severely biased and hence estimating the probability of rare events occurring based on a logistic regression model would be inaccurate. In this article, we focus on estimating the probability of rare events occurring based on logistic regression models. Instead of selecting a best model, we propose a local model averaging procedure based on a data perturbation technique applied to different information criteria to obtain different probability estimates of rare events occurring. Then an approximately unbiased estimator of Kullback‐Leibler loss is used to choose the best one among them. We design complete simulations to show the effectiveness of our approach. For illustration, a necrotizing enterocolitis (NEC) data set is analyzed.