Asymptotic Normality of a Combined Regression Estimator

In this paper, we propose a combined regression estimator by using a parametric estimator and a nonparametric estimator of the regression function. The asymptotic distribution of this estimator is obtained for cases where the parametric regression model is correct, incorrect, and approximately correct. These distributional results imply that the combined estimator is superior to the kernel estimator in the sense that it can never do worse than the kernel estimator in terms of convergence rate and it has the same convergence rate as the parametric estimator in the case where the parametric model is correct. Unlike the parametric estimator, the combined estimator is robust to model misspecification. In addition, we also establish the asymptotic distribution of the estimator of the weight given to the parametric estimator in constructing the combined estimator. This can be used to construct consistent tests for the parametric regression model used to form the combined estimator.     

Option pricing under a normal mixture distribution derived from the Markov tree model

We examine a Markov tree (MT) model for option pricing in which the dynamics of the underlying asset are modeled by a non-IID process. We show that the discrete probability mass function of log returns generated by the tree is closely approximated by a continuous mixture of two normal distributions. Using this normal mixture distribution and risk-neutral pricing, we derive a closed-form expression for European call option prices. We also suggest a regression tree-based method for estimating three volatility parameters σ, σ⁺, and σ⁻ required to apply the MT model. We apply the MT model to price call options on 89 non-dividend paying stocks from the S&P 500 index. For each stock symbol on a given day, we use the same parameters to price options across all strikes and expires. Comparing against the Black–Scholes model, we find that the MT model’s prices are closer to market prices.     

一类随机利率下的增额寿险

寿险中的利率随机问题，是近来保险精算研究的热点和重点问题之一。本以即时给付的一类增额寿险为对象，对随机利率采用Gauss过程建模，研究给付现值及其各阶矩。     

关于两个伪硬币问题的一些新结果

下面的问题被称为n个外观不可区分硬币的分组测试问题,每个硬币可以是伪硬币或是标准硬币.本文所涉及的问题是:已知一个由n个硬币组成的集合中有两个伪(较重的)硬币,用一台天平以最小的称重次数,从这n个硬币组成的集合中探测出两个伪(较重的)硬币. 我们构造了找出两个伪(较重的)硬币的两个算法,并且这两个算法是最优的.     

涂层界面结合强度检测研究(Ⅰ):涂层结合界面应力的理论分析

利用激光划痕测试法和弯曲应力理论，建立了涂层结合界面应力的理论模型，推导出结合界面剪应力、正应力和剥离应力分布公式，分析了结合界面应力产生的机理.理论分析结果表明，界面正应力主要集中在界面中心区域内，而在界面边缘附近，正应力迅速下降，在界面边缘处其值降为0；剪应力和剥离应力主要集中在界面边缘区域内，在远离界面边缘区域，剪应力和剥离应力则迅速下降；涂层中正应力和涂层厚度、基体厚度以及杨氏模量无关，界面间剪应力以及剥离应力随涂层厚度增加而增加，并且由涂层与基体厚度以及杨氏模量所共同决定.     

物理学新技术与生命科学前沿研究

物理学新技术对现代生命科学取得的巨大成就发挥了极其重要的作用；生命科学和医学的需要又促进了物理新技术和仪器的发展。本文介绍了在生命科学一些前沿研究领域中应用物理学新技术取得的成果，并展望了21世纪的发展前景，包括：单分子动力学、结构生物学、纳米技术和生物技术、分子细胞信息学、脑科学和神经信息学。     

LY2铝合金激光冲击处理工艺

为了提高LY2铝合金疲劳寿命,探索了用激光冲击强化方法处理LY2铝合金的技术。使用流水约束层和铝箔涂层,利用波长为532 nm,脉宽为10 ns,能量为5 J,光斑直径为3 mm的YAG激光器对LY2试样进行了激光冲击强化处理。结果表明:经激光冲击强化后LY2铝合金疲劳寿命提高1.1倍。对激光冲击强化后的试件断口分析结果表明激光冲击的强化作用抑制了裂纹在试件表层的萌生和扩展,从而提高了材料的疲劳寿命。     

复合材料脱粘缺陷的红外热像无损检测

以固体火箭发动机中的玻璃纤维复合材料壳体/绝热层试件的脱粘缺陷为研究对象,利用脉冲闪光灯热激励方式对试件进行加热,用红外热像仪实时监测试件的表面温度场,由表面温度差异来判定试件内部缺陷,然后通过对热像图进行图像增强处理和分割以定量识别缺陷。将实验结果与超声C扫描检测结果进行的对比分析表明:红外热像无损检测方法能够快速直观地发现深度5 mm以内、直径10 mm以上的脱粘缺陷,而超声C扫描检测更适合于对特定缺陷进行准确定量检测。     

Esaki diodes live and learn

We report, as the result of shelf-life tests for Esaki diodes, the observation of minute but tangible reductions in the tunnel current after the lapse of half a century. The reduction could be attributed to 0.25% widening in the tunnel path.     

对数正态分布基于恒加试验的参数估计

讨论对数正态分布场合有非常数尺度参数恒加试验的参数估计,由最小均方误差准则导出基于完全样本恒加试验的点估计和近似区间估计.