Mildly explosive autoregression with mixing innovations

In this paper, the limit distribution of the least squares estimator for mildly explosive autoregressive models with strong mixing innovations is established, which is shown to be Cauchy as in the iid case. The result is applied to identify the onset and the end of an explosive period of an econometric time series. Simulations and data analysis are also conducted to demonstrate the usefulness of the result.     

Limit Theorems for the Counting Process of Near-Records

Near-records of a sequence, as defined in Balakrishnan et al. (2005 Balakrishnan , N. , Pakes , A. G. , Stepanov , A. ( 2005 ). On the number and sum of near-record observations . Advances in Applied Probability 37 : 765 – 780 .[Crossref], [Web of Science ®] , [Google Scholar]), are observations lying within a fixed distance of the current record. In this article we study the asymptotic behavior of the number of near-records, among the first n observations in a sequence of independent, identically distributed and absolutely continuous random variables. We give conditions for the finiteness of the total number of near-records as well as laws of large numbers for their counting process. For distributions with a finite number of near-records, we carry out a simulation study suggesting that the total number of near-records has a geometric distribution.     

会计盈余信息选拔的不可能定理及其理论深化

在美国等较为成熟的股票市场中,会计盈余信息的主要功能是定价,但是在政府管制较为严格的中国股票市场中,会计盈余信息的首要功能是"选拔".文章对中国证监会和上市公司之间的博弈构建了数学模型,提出了"会计盈余信息选拔的不可能定理":若以下两个条件存在--(1)监管部门设定选拔标准有一个上限,或(2)上市公司通过合法或非法方式虚报盈余的"虚报成本系数"较小,那么单纯依靠会计盈余信息将无法选拔出理想的目标公司.由此得到的可能的理论深化是:度量标准应当与契约形式相配合,如果制度环境本身限制了会计盈余信息作为度量标准的使用,那么改善的要点不应该集中于会计盈余信息本身,而应该在于调整博弈制度的安排.同时也为中国新会计准则执行研究提供了新的理论视角.     

The Realized Laplace Transform of Volatility

We introduce and derive the asymptotic behavior of a new measure constructed from high‐frequency data which we call the realized Laplace transform of volatility. The statistic provides a nonparametric estimate for the empirical Laplace transform function of the latent stochastic volatility process over a given interval of time and is robust to the presence of jumps in the price process. With a long span of data, that is, under joint long‐span and infill asymptotics, the statistic can be used to construct a nonparametric estimate of the volatility Laplace transform as well as of the integrated joint Laplace transform of volatility over different points of time. We derive feasible functional limit theorems for our statistic both under fixed‐span and infill asymptotics as well as under joint long‐span and infill asymptotics which allow us to quantify the precision in estimation under both sampling schemes.     

从Fermat大定理看数学问题在数学发展中的作用

回顾费马大定理的解决过程,从一个侧面论述了数学问题对数学发展的推动作用.     

三阶线性齐次微分方程解的Sturm比较定理

研究了一类三阶线性齐次微分方程解的Sturm比较定理的理论,在三阶线性齐次微分方程的Sturm基本定理的基础上得出了Sturm比较定理,所得结论推广了现有文献的相应结果。     

Functional Estimation of a Density Under a New Weak Dependence Condition

The purpose of this paper is to prove, through the analysis of the behaviour of a standard kernel density estimator, that the notion of weak dependence defined in a previous paper (cf. Doukhan & Louhichi, 1999) has sufficiently sharp properties to be used in various situations. More precisely we investigate the asymptotics of high order losses, asymptotic distributions and uniform almost sure behaviour of kernel density estimates. We prove that they are the same as for independent samples (with some restrictions for a.s. behaviours). Recall finally that this weak dependence condition extends on the previously defined ones such as mixing, association and it allows considerations of new classes such as weak shifts processes based on independent sequences as well as some non-mixing Markov processes.     

Boosting with Bayesian stumps

Boosting is a new, powerful method for classification. It is an iterative procedure which successively classifies a weighted version of the sample, and then reweights this sample dependent on how successful the classification was. In this paper we review some of the commonly used methods for performing boosting and show how they can be fit into a Bayesian setup at each iteration of the algorithm. We demonstrate how this formulation gives rise to a new splitting criterion when using a domain-partitioning classification method such as a decision tree. Further we can improve the predictive performance of simple decision trees, known as stumps, by using a posterior weighted average of them to classify at each step of the algorithm, rather than just a single stump. The main advantage of this approach is to reduce the number of boosting iterations required to produce a good classifier with only a minimal increase in the computational complexity of the algorithm.     

高阶拟线性时滞差分方程非振动解的存在性与渐近性

考虑一类高阶拟线性时滞差分方程给出了存在无界非振动解的充要条件。     

Combinatorial Optimization in Real-Time Scheduling: Theory and Algorithms

Real-time computer systems are essential for many applications, such as robot control, avionics, medical instrumentation, manufacturing, etc. The correctness of the system depends on the temporal correctness as well as the functional correctness of the task executions. In order to assure temporal correctness it is necessary that the resources be scheduled to meet the temporal requirements of applications. When we consider the problem of nonpreemptive scheduling of a set of tasks in a processor for which no feasible solution exists, some tasks may have to be rejected so that a schedule can be generated for the rest. In this paper, we consider the problem of generating an optimal schedule such that the number of rejected tasks is minimized, and then the finish time is minimized for the accepted tasks. We propose to use an analytic approach to solve this problem. We first discuss the super sequence based technique which was originally proposed for reducing the search space in testing the feasibility of a task set. Then we show by the Conformation theorem that the super sequence constructed from the task set also provides a valid and reduced search space for the optimization problem. While the complexity of our scheduling algorithm in the worst case remains exponential, our simulation results show that the cost is reasonable for the average case.