Tokamak中自举电流的剖面准直性

利用Harris模型,通过求解等离子体平衡方程,计算俘获粒子份额,分别对常规剪切和中心负剪切下tokamak中的自举电流的大小和剖面准直性进行了计算和分析.自举电流分布与等离子体平衡电流分布之间的剖面准直性可以通过调整等离子体的密度、温度和电流分布参数,以及描述等离子体形状的拉长度k和三角变形因子d来获得.中心负剪切位形有利于自举电流产生,并有好的剖面准直性.通过计算比较,分别在常规剪切位形下和中心负剪切位形下获得了一组优化的等离子体参数,在这组参数下,自举电流有较大的份额和好的剖面准直性 关键词： tokamak 自举电流 剖面准直性     

中国银行间拆借利率扩散模型的极大拟似然估计

本文用极大拟似然估计法估计了中国银行间市场七天拆借利率扩散模型的参数。并用自助法对众多不同的模型进行了广义拟似然比检验。结论表明:中国货币市场利率具有均值回复效应:利率敏感系数γ值为1.421265,对利率水平具有较高敏感性。     

Size dependence of self-diffusion in the hard-square lattice gas

A striking size dependence of the mean-square displacement of diffusing particles in the two-dimensional lattice gas of hard squares has been observed by Monte Carlo simulation. It is shown that the size effect is due to the formation of a stable cage structure in small lattices when the particle concentration is high. The formation of cages is governed by a new type of percolation problem related to bootstrap percolation.     

价格与隐含远期违约概率

如同根据布莱克-斯科尔斯模型从欧式看涨期权市场价格中反求隐含波动率一样,从信用违约互换的价格中提取隐含违约概率在理论上和实践上都存在很多困难.传统的自助法存在很大的缺点,并有可能得出不符合现实的结果.本文采用基于一段时期的条件违约概率的新的优化方法来替代基于自助法的瞬时远期违约概率,该方法有很多优良特性,会得出比传统方法好得多的结论.     

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用TSC程序模拟了EAST装置等离子体放电的全过程。模拟中考虑了自举电流，并加入了离子回旋共振加热ICRH和快波电流驱动FWCD，得到了中心电子温度4.5keV、中心离子温度3.8keV、中心电子密度1.2×1020m–3的D形截面的等离子体。根据模拟结果对EAST装置进行了伏秒数分析，并研究了不同等离子体电流上升时间、有效电荷数Zeff对放电的影响。     

Bootstrapping the empirical distribution of a linear process

The validity of the moving block bootstrap for the empirical distribution of a short memory causal linear process is established under simple conditions that do not involve mixing or association. Sufficient conditions can be expressed in terms of the existence of moments of the innovations and summability of the coefficients of the linear model. Applications to one and two sample tests are discussed.     

Likelihood Inference under Generalized Hybrid Censoring Scheme with Comp eting Risks

Statistical inference is developed for the analysis of generalized type-II hybrid censoring data under exponential competing risks model. In order to solve the problem that approximate methods make unsatisfactory performances in the case of small sample size, we establish the exact conditional distributions of estimators for parameters by conditional moment generating function(CMGF). Furthermore, confidence intervals(CIs) are constructed by exact distributions, approximate distributions as well as bootstrap method respectively, and their performances are evaluated by Monte Carlo simulations. And finally, a real data set is analyzed to illustrate all the methods developed here.     

A new test on the conditional capital asset pricing model

Testing the validity of the conditional capital asset pricing model(CAPM) is a puzzle in the finance literatureLewellen and Nagel[14]find that the variation in betas and in the equity premium would have to be implausibly large to explain important asset-pricing anomaliesUnfortunately, they do not provide a rigorous test statisticBased on a simulation study, the method proposed in Lewellen and Nagel[14]tends to reject the null too frequently.We develop a new test procedure and derive its limiting distribution under the null hypothesis.Also, we provide a Bootstrap approach to the testing procedure to gain a good finite sample performanceBoth simulations and empirical studies show that our test is necessary for making correct inferences with the conditional CAPM.     

AN EFFICIENT SEQUENTIAL DESIGN FOR SENSITIVITY EXPERIMENTS

In sensitivity experiments, the response is binary and each experimental unit has a critical stimulus level that cannot be observed directly. It is often of interest to estimate extreme quantiles of the distribution of these critical stimulus levels over the tested products. For this purpose a new sequential scheme is proposed with some commonly used models. By using the bootstrap repeated-sampling principle, reasonable prior distributions based on a historic data set are specified. Then, a Bayesian strategy for the sequential procedure is provided and the estimator is given. Further, a high order approximation for such an estimator is explored and its consistency is proven. A simulation study shows that the proposed method gives superior performances over the existing methods.     

The Bootstrap Methodology in Statistics of Extremes—Choice of the Optimal Sample Fraction

The main objective of statistics of extremes is the prediction of rare events, and its primary problem has been the estimation of the tail index , usually performed on the basis of the largest k order statistics in the sample or on the excesses over a high level u. The question that has been often addressed in practical applications of extreme value theory is the choice of either k or u, and an adaptive estimation of . We shall be here mainly interested in the use of the bootstrap methodology to estimate adaptively, and although the methods provided may be applied, with adequate modifications, to the general domain of attraction of G, , we shall here illustrate the methods for heavy right tails, i.e. for > 0. Special relevance will be given to the use of an auxiliary statistic that is merely the difference of two estimators with the same functional form as the estimator under study, computed at two different levels. We shall also compare, through Monte Carlo simulation, these bootstrap methodologies with other data-driven choices of the optimal sample fraction available in the literature.