分位数单位根检验的拓展及其应用研究

在不同的分位下对时间序列数据是否含有结构性变化进行甄别,对于准确识别数据的动态变化及其分布特征具有重要的意义.本文首次在Koenker和Xiao(2004)研究的基础上提出傅立叶分位数单位根检验模型,并以此捕捉时间序列中存在的结构突变点,进而刻画数据在不同分位下的动态变化特征.本文通过构建傅立叶QKS统计量并采用蒙特卡罗方法对傅立叶分位数模型的临界值、样本容量和检验“势”进行模拟,发现含有傅立叶级数的分位数单位根检验对刻画“尖峰厚尾”特征数据的非线性偏离动态调节特征具有更高的检验“势”.最后,本文利用拓展后的模型对我国通货膨胀的持久性和失业的回滞效应进行再检验,结果发现我国通货膨胀具有平稳的特征,而失业率却包含单位根过程.本研究为分位数单位根检验的拓展及其应用提供了一定的启示.     

存在领导者的自组织群组评价技术及应用

针对传统群组评价中存在的主客观约束问题和领导者的“超权威”效应,本文提出了一种存在领导者的自组织群组评价技术.首先,群组专家进行选择性评价,以克服客体不同质对主体造成的认知困境;其次,根据评价的信度与效度修正专家意见,并通过自回归函数进行迭代,使其与领导者意见达成一致,以避免因时间限制或专家不愿修改意见而对评价活动产生影响,另外,根据加权后的一致性意见进行评价,能兼顾领导者的权威性与群组成员表达的充分性;最后,给出了应用实例.结果表明本文方法简单、可操作,更有利于大规模群组评价活动的开展.     

Limit theorems for dependent Bernoulli variables with statistical inference

We consider a class of dependent Bernoulli variables where the conditional success probability is a linear combination of the last few trials and the original success probability. We obtain its limit theorems including the strong law of large numbers, weak invariance principle, and law of the iterated logarithm. We also derive some statistical inference results which make the model applicable. Simulation results are exhibited as well to show that with small sample size the convergence rate is satisfying and the proposed estimators behave well.     

关于检验抗病毒药物有效性样本量的比较

文章基于链式二项式模型,运用符号秩和检验法,将检验抗病毒药物的有效性及所需样本容量与Alexander-Govern检验进行了比较研究,并分析了抗病毒药物的不同控制策略对检验抗病毒药物有效性所需样本容量的影响.研究表明,符号秩和检验所需样本量比becker和wang所用的Alexander-Govern检验所需样本量更少,并且采用治疗加预防策略且尽早发放药物可有效减少样本容量.     

灰色多级排队供应链系统的稳定性测度方法

文章以供应链网络为研究基础,建立了一种灰色多级排队供应链系统,并采用多级排队网络方法研究受到达率和服务时间间隔两个扰动量影响时该系统的稳定性测度方法.验证结果显示:对于受到扰动量影响的灰色多级排队供应链系统,均能快速达到稳定状态,且扰动量能改善供应链系统的整体性能,即使出现异常值,该值变化的波动性也较小;在稳定状态后因具有相对固定量的库存水平,系统不再受其他因素的制约,显示了较强的鲁棒性特征.     

Optimal control strategies for dividend payments and capital injections in compound Markov binomial risk model with penalties for deficits

We consider the compound Markov binomial risk model. The company controls the amount of dividends paid to the shareholders as well as the capital injections in order to maximize the cumulative expected discounted dividends minus the discounted capital injections and the discounted penalties for deficits prior to ruin. We show that the optimal value function is the unique solution of an HJB equation, and the optimal control strategy is a two-barriers strategy given the current state of the Markov chain. We obtain some properties of the optimal strategy and the optimal condition for ruining the company. We offer a high-efficiency algorithm for obtaining the optimal strategy and the optimal value function. In addition, we also discuss the optimal control problem under a restriction of bounded dividend rates. Numerical results are provided to illustrate the algorithm and the impact of the penalties.     

中国金融产业发展的就业效应研究

文章基于2004-2015年我国31个省市的面板数据,应用空间计量模型研究了我国金融产业的就业效应.研究结果表明,金融集聚、金融发展规模和金融发展效率对金融业就业增长有显著的正效应,且金融集聚与金融发展规模和效率之间的交互作用能促进金融业就业的增长,但金融集聚与金融发展规模之间的交互作用要强于与金融发展效率之间的作用.     

Generalized RCINAR(1) Process with Signed Thinning Operator

A generalized random coefficient first-order integer-valued autoregressive process with signed thinning operator is introduced, this kind of process is appropriate for modeling negative integer-valued time series. Strict stationarity and ergodicity of process are established. Estimators of the parameters of interest are derived and their properties are studied via simulation. At last, we use bootstrap method in the real data analysis.     

A three-stage variable selection method for supersaturated designs

A supersaturated design (SSD) is a design whose run size is not enough for estimating all main effects. Such a design is commonly used in screening experiments to screen active effects based on the effect sparsity principle. Traditional approaches, such as the ordinary stepwise regression and the best subset variable selection, may not be appropriate in this situation. In this article, a new variable selection method is proposed based on the idea of staged dimensionality reduction. Simulations and several real data studies indicate that the newly proposed method is more effective than the existing data analysis methods.     

Bayesian ratemaking under Dirichlet process mixtures

Experience ratemaking plays a crucial role in general insurance in determining future premiums of individuals in a portfolio by assessing observed claims from the whole portfolio. This paper investigates this problem in which claims can be modeled by certain parametric family of distributions. The Dirichlet process mixtures are employed to model the distributions of the parameters so as to make two advantages: to produce exact Bayesian experience premiums for a class of premium principles generated from generic error functions and, at the same time, provide robust and flexible ways to avoid possible bias caused by traditionally used priors such as non informative priors or conjugate priors. In this paper, the Bayesian experience ratemaking under Dirichlet process mixture models are investigated and due to the lack of analytical forms of the conditional expectations of the quantities concerned, the Gibbs sampling schemes are designed for the purpose of approximations.