基于模糊综合评判收益约束的贷款组合优化模型

贷款组合优化是商业银行信贷管理中最常见的决策。本文分析了现有的贷款组合优化模型的特点和弊端,以组合风险最小化为目标,以模糊数学中的综合评判关系为约束条件,建立了贷款组合的模糊优化模型。在组合收益率的综合评判目标和评判矩阵已确定的前提条件下,利用二次规划的方法解出各类贷款额占总贷款额的比重,解决了银行各类贷款的组合决策问题。通过进一步的实例分析,又从实证角度说明了该方法的合理性和可行性。     

基于社会需求的高校毕业生就业竞争力研究

社会需求是检验高校毕业生就业竞争力的"试金石"。本文在文献综述的基础上,通过对社会需求单位的调查,构建了高校毕业生就业竞争力评价指标体系;运用层次分析法(AHP)确定了高校毕业生就业竞争力评价指标权重;最后,以某高校为例,采用模糊综合评价方法进行了实证分析,证明了评价指标体系具有一定的实用性和可操作性。     

面向现代航空制造需求的飞行器制造工程专业本科培养方案改革——基于南京航空航天大学实践的思考

现代航空制造技术的发展对本科生的知识结构和培养模式等方面提出了许多新的要求,最主要体现在对专业知识的系统性、课程内容的前沿性、培养模式的实践性等环节的提升。改革的思路沿着现代航空制造技术的发展主线,拓展飞行器制造工程专业的前沿知识框架,建立突出实践环节的模块化专业课程群,提出面向职业规划的套餐式选课,创新综合成绩绩点的评估方法。     

An effective approach to linear calibration estimation with its applications

Abstract

The availability of some extra information, along with the actual variable of interest, may be easily accessible in different practical situations. A sensible use of the additional source may help to improve the properties of statistical techniques. In this study, we focus on the estimators for calibration and intend to propose a setup where we reply only on first two moments instead of modeling the whole distributional shape. We have proposed an estimator for linear calibration problems and investigated it under normal and skewed environments. We have partitioned its mean squared error into intrinsic and estimation components. We have observed that the bias and mean squared error of the proposed estimator are function of four dimensionless quantities. It is to be noticed that both the classical and the inverse estimators become the special cases of the proposed estimator. Moreover, the mean squared error of the proposed estimator and the exact mean squared error of the inverse estimator coincide. We have also observed that the proposed estimator performs quite well for skewed errors as well. The real data applications are also included in the study for practical considerations.     

Robust Bayesian analysis for parallel system with masked data under inverse Weibull lifetime distribution

Abstract

This paper considers the statistical analysis of masked data in a parallel system with inverse Weibull distributed components under type II censoring. Based on Gamma conjugate prior, the Bayesian estimation as well as the hierarchical Bayesian estimation for the parameters and the reliability function of system are obtained by using the Bayesian theory and the hierarchical Bayesian method. Finally, Monte Carlo simulations are provided to compare the performances of the estimates under different masking probabilities and effective sample sizes.     

A permutation-based Bayesian approach for inverse covariance estimation

Abstract

Covariance estimation and selection for multivariate datasets in a high-dimensional regime is a fundamental problem in modern statistics. Gaussian graphical models are a popular class of models used for this purpose. Current Bayesian methods for inverse covariance matrix estimation under Gaussian graphical models require the underlying graph and hence the ordering of variables to be known. However, in practice, such information on the true underlying model is often unavailable. We therefore propose a novel permutation-based Bayesian approach to tackle the unknown variable ordering issue. In particular, we utilize multiple maximum a posteriori estimates under the DAG-Wishart prior for each permutation, and subsequently construct the final estimate of the inverse covariance matrix. The proposed estimator has smaller variability and yields order-invariant property. We establish posterior convergence rates under mild assumptions and illustrate that our method outperforms existing approaches in estimating the inverse covariance matrices via simulation studies.     

Improved methods for moment restriction models with data combination and an application to two-sample instrumental variable estimation

Combining-100 information from multiple samples is often needed in biomedical and economic studies, but differences between these samples must be appropriately taken into account in the analysis of the combined data. We study the estimation for moment restriction models with data combined from two samples under an ignorability-type assumption while allowing for different marginal distributions of variables common to both samples. Suppose that an outcome regression (OR) model and a propensity score (PS) model are specified. By leveraging semi-parametric efficiency theory, we derive an augmented inverse probability-weighted (AIPW) estimator that is locally efficient and doubly robust with respect to these models. Furthermore, we develop calibrated regression and likelihood estimators that are not only locally efficient and doubly robust but also intrinsically efficient in achieving smaller variances than the AIPW estimator when the PS model is correctly specified but the OR model may be mispecified. As an important application, we study the two-sample instrumental variable problem and derive the corresponding estimators while allowing for incompatible distributions of variables common to the two samples. Finally, we provide a simulation study and an econometric application on public housing projects to demonstrate the superior performance of our improved estimators. The Canadian Journal of Statistics 48: 259–284; 2020 © 2019 Statistical Society of Canada     

物理教学中学生综合思维和动态思维能力的培养

物理实验和解题中的误区说明了在物理学习中,培养学生的综合思维能力和动态思维能力的方法及其重要性.     

线性度对线性调频雷达距离分辨力的影响

讨论了线性度对线性调频雷达距离分辨力的影响,求得了线性调频雷达的极限分辨力,导出了调频非线性对距离分辨力影响的数学表达式,以及用于工程的近似估计公式。用计算机仿真验证了这种影响。仿真结果和理论分析表明,距离分辨力随以下三个参数的增大而降低:国标距离,频率偏离函数F(t)的高次谐波和最大值。线性调频雷达的极限分辨力取决于发射波形的带宽。