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本文讨论了定时截尾样本下三参数Weibull分布修正矩估计(MME)的强相合性.首先证明了修正样本矩的强相合性.然后给出了条件(L),得出结论:若所研究的分布F(x;θ1,θ2,θ3)满足条件(L),修正矩估计θ1,θ2,θ3强相合于参数真值.最后证明了当形状参数δ≥1,即失效率增加时,三参数威布尔分布Wei(x;β,δ,γ)满足条件(L),即MME是强相合的.     

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在线性回归模型建模中, 回归自变量选择是一个受到广泛关注、文献众多, 具有很强的理论和实际意义的问题. 回归自变量选择子集的相合性是其中一个重要问题, 如果某种自变量选择方法选择的子集在样本量趋于无穷时是相合的, 而且预测均方误差较小, 则这种方法是可取的. 利用BIC准则可以挑选相合的自变量子集, 但是在自变量个数很多时计算量过大; 适应lasso方法具有较高计算效率, 也能找到相合的自变量子集; 本文提出一种更简单的自变量选择方法, 只需要计算两次普通线性回归: 第一次进行全集回归, 得到全集的回归系数估计, 然后利用这些回归系数估计挑选子集, 然后只要在挑选的自变量子集上再进行一次普通线性回归就得到了回归结果. 考虑如下的回归模型: 其中回归系数中非零分量下标的集合为, 设是本文方法选择的自变量子集下标集合, 是本文方法估计的回归系数(未选中的自变量对应的系数为零), 本文证明了, 在适当条件下, 其中表示的 分量下标在中的元素的组成的向量, 是误差方差, 是与 矩阵极限有关的矩阵和常数. 数值模拟结果表明本文方法具有很好的中小样本性质.     

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本文讨论了右半直线上带有原点反射壁的随机环境中随机游动,并给出了其常返性的充要条件.在非常返的情况下,获得了{Xn}从n-1到n首中时τn的二阶矩Eτn2的一个估计.同时,给出了右半直线上随机环境中随机游动的强大数定律.     

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??This paper extends a class of discount problem of singular
stochastic control with stopping time. We extend the state process and cost function
to general case. By stochastic analysis and optimal control theory, the "fail-stop"
control strategy is its optimal control. The conditions of the "fail-stop" strategy
and optimal control function and control method are given. The conclusion in this
paper has a fairly deep application.     

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Cardy给出临界渝流族横穿一个长方形 而不碰到长方形的上边和下边的概率估计公式; Lawler, Schramm和Werner给出了参数的通弦随机Loewner演 变穿过长方形的类似的概率估计公式. 在本文, 我们将后者的结果推广到的情形.     

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Let,,,
be all independent PRHR variables. Firstly, we show thatimplies. Secondly, we consider the comparison of
convolutions of independent heterogeneous PRHR variables with respect to the usual stochastic
ordering. Suppose and, we prove that implies,
for all. The results established here strengthen some of the results known in
the literature.     

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研究了负相关随机变量阵列加权和的矩完全收敛性,改进了Baek等(2008)的结果.作为应用,得到了基于负相关随机变量序列的平滑移动过程的矩完全收敛性,完善了Li等(2004)的结果.     

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利用鞅方法,我们给出跳扩散过程的偏差不等式,推广了之前关于纯Lévy跳过程在类Cramér条件下的结论,同时我们的方法对于Lévy测度不具有指数矩的情形也是适用的.     

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本文证明了copula , 生存copula , 对偶copula 和伴随copula 关于copula的复合运算构成一个四元群, 给出了当某个单点值给定时它们的最优上下界. 计算了, 时copula最优上下界的宽窄度, 并与时的宽窄度进行了比较.     

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??Recurrent event data usually occur in long-term studies which concern
recurrence rates of the disease. In studies of medical sciences, patients who have infected
with the disease, like cancer, were conventionally regarded as impossible to be cured. However,
with the development of medical sciences, recently those patients were found to be possibly
recovered from the disease. The recurrence rate of the events, which is of primary interest,
may be affected by the cure rate that may exist. Therefore, we proposed semiparametric
statistical analysis for recurrent event data with subjects possibly being cured. In our
approach, we present a proportional rate model for recurrence rate with the cure rate adjusted
through a Logistic regression model, and develop some estimating equations for estimation of
the regression parameters, with their large sample properties, including consistency and
asymptotic normality established. Numerical studies under different settings were conducted
for assessing the proposed methodology and the results suggest that they work well for
practical situations. The approach is applied to a bladder cancer dataset which motivated our
study.