用E-L方法计算圆平头物体垂直入水空泡的面闭合和深闭合

本文根据非线性自由面理论,用E-L方法计算了圆平头物体垂直入水空泡发生、发展、面闭合、拉离和深闭合等一系列连续变化过程。空泡中气体流动分为三个阶段加以处理。采用Riabouchinsky映象板模型,初步解决了高F_r数下数值模拟空泡深闭合的困难。计算结果表明,对于给定的物体头型,F_r数、空气出度数、物体质量数是影响入水空泡发展过程的主要因素。     

Robust non-zero-sum investment and reinsurance game with default risk

This paper investigates a non-zero-sum stochastic differential game between two competitive CARA insurers, who are concerned about the potential model ambiguity and aim to seek the robust optimal reinsurance and investment strategies. The ambiguity-averse insurers are allowed to purchase reinsurance treaty to mitigate individual claim risks; and can invest in a financial market consisting of one risk-free asset, one risky asset and one defaultable corporate bond. The objective of each insurer is to maximize the expected exponential utility of his terminal surplus relative to that of his competitor under the worst-case scenario of the alternative measures. Applying the techniques of stochastic dynamic programming, we derive the robust Nash equilibrium reinsurance and investment policies explicitly and present the corresponding verification theorem. Finally, we perform some numerical examples to illustrate the influence of model parameters on the equilibrium reinsurance and investment strategies and draw some economic interpretations from these results.     

Restricted Maximum Likelihood Estimates in Finite Mixture Models

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货物吞吐量预测的改进ARIMAX方法——以天津港为例

为提高港口货物吞吐量预测精度,建立了基于ARIMAX-SVR的组合预测模型。以天津港为例,选取1999～2018年货物吞吐量数据进行分析,首先运用BP神经网络补插缺失数据,然后通过Pearson相关分析筛选出影响货物吞吐量的主要因素;再在ARIMA模型的基础上建立了ARIMAX模型,为进一步提高模型精度,最后建立了SVR模型修正的ARIMAX模型。实证分析结果表明组合模型拟合精度更高,预测效果更好,适用于港口吞吐量预测并且模型具有一定的先进性。     

Bayesian Inference for the One-Factor Copula Model

We develop efficient Bayesian inference for the one-factor copula model with two significant contributions over existing methodologies. First, our approach leads to straightforward inference on dependence parameters and the latent factor; only inference on the former is available under frequentist alternatives. Second, we develop a reversible jump Markov chain Monte Carlo algorithm that averages over models constructed from different bivariate copula building blocks. Our approach accommodates any combination of discrete and continuous margins. Through extensive simulations, we compare the computational and Monte Carlo efficiency of alternative proposed sampling schemes. The preferred algorithm provides reliable inference on parameters, the latent factor, and model space. The potential of the methodology is highlighted in an empirical study of 10 binary measures of socio-economic deprivation collected for 11,463 East Timorese households. The importance of conducting inference on the latent factor is motivated by constructing a poverty index using estimates of the factor. Compared to a linear Gaussian factor model, our model average improves out-of-sample fit. The relationships between the poverty index and observed variables uncovered by our approach are diverse and allow for a richer and more precise understanding of the dependence between overall deprivation and individual measures of well-being.     

Outlier Detections in Autoregressive Models

This article proposes a new technique for detecting outliers in autoregressive models and identifying the type as either innovation or additive. This technique can be used without knowledge of the true model order, outlier location, or outlier type. Specifically, we perturb an observation to obtain the perturbation size that minimizes the resulting residual sum of squares (SSE). The reduction in the SSE yields outlier detection and identification measures. In addition, the perturbation size can be used to gauge the magnitude of the outlier. Monte Carlo studies and empirical examples are presented to illustrate the performance of the proposed method as well as the impact of outliers on model selection and parameter estimation. We also obtain robust estimators and model selection criteria, which are shown in simulation studies to perform well when large outliers occur.     

The space of strong types and an open map theorem

We define a new topology on the space of strong types on a subsetA of a model of a given theory and prove that either . We also deduce an open map theorem.     

线性约束优化问题的开关算法模型及其应用

本文利用开关函数．建立了解线性约束优化问题的一个组合型可行方向法─—开关算法模型，并给出了其收敛性质，从而统一、推广了包括起线性收敛的算法在内的常见的可行方向法．依此模型，具体构造了一类起线性收敛的新算法．     

随机利率条件下最优投资消费与寿险购买策略

随着我国利率市场化的深入发展, 利率的随机波动对投资者的最优投资消费策略将产生重要影响. 与此同时, 随着我国寿险市场的渐趋完善, 寿险购买也越来越受到投资者的重视, 投资者的最优策略也将发生改变. 现研究由 Vasicek 模型来刻画的随机利率条件下最优投资消费与寿险购买策略. 投资者的目标在于选择最优投资消费与寿险购买策略使期望效用最大化. 通过运用 Legendre 转换方法求出最优投资消费与寿险购买的显性解. 通过数值分析的方法, 实证分析相关变量的变化对投资者最优投资与寿险购买策略的影响.