非线性协和模型：理论与方法

 本文系统梳理了十多年来非线性协和理论与方法的研究进展,从非线性协和概念的提出开始,对目前已给出的非线性协和的各种不同定义、具有非线性结构的时间序列的非平稳性检验、非线性协和关系的检验、非线性协和模型的参数与非参数估计方法、门限协和模型、以及非线性误差修正模型的研究状况进行了总结。     

多民族中国古代文学史教材编撰侧议

中华民族文学是多民族文学构成的共同体,具有多样统一的特点.一般来说,目前流行的高校文科中国古代文学史教材绝大多数没有充分注意到中国文学的多民族性这一特点,所述内容基本上是汉族文学及少量的少数民族作家的汉文创作.亟须编撰尽可能真正再现中华多民族文学原生态和民族特点的《中国古代文学史》.正确认识并解决好相关的理论问题是编撰多民族文学史教材的前提.应以多民族文学史观为指导,从民族融合和民族文化融合的切入点审视中国古代文学现象及其流程,对古代少数民族的作家书面文学和民间文学口头创作都给予足够的重视.基础研究和各民族文学专史的编写是综合性的多民族《中国古代文学史》产生的必要条件.     

Strong laws of large numbers for dependent heterogeneous processes: a synthesis of recent and new results

This paper surveys recent developments in the strong law of large numbers for dependent heterogeneous processes. We prove a generalised version of a recent strong law for Lz-mixingales, and also a new strong law for Lpmixingales. These results greatly relax the dependence and heterogeneity conditions relative to those currently cited, and introduce explicit trade-offs between dependence and heterogeneity. The results are applied to proving strong laws for near-epoch dependent functions of mixing processes. We contrast several methods for obtaining these results, including mapping directly to the mixingale properties, and applying a truncation argument.     

Estimating Mixing Densities in Exponential Family Models for Discrete Variables

This paper is concerned with estimating a mixing density g using a random sample from the mixture distribution f(x)=∫f x | θ)g(θ)dθ where f(· | θ) is a known discrete exponen tial family of density functions. Recently two techniques for estimating g have been proposed. The first uses Fourier analysis and the method of kernels and the second uses orthogonal polynomials. It is known that the first technique is capable of yielding estimators that achieve (or almost achieve) the minimax convergence rate. We show that this is true for the technique based on orthogonal polynomials as well. The practical implementation of these estimators is also addressed. Computer experiments indicate that the kernel estimators give somewhat disappoint ing finite sample results. However, the orthogonal polynomial estimators appear to do much better. To improve on the finite sample performance of the orthogonal polynomial estimators, a way of estimating the optimal truncation parameter is proposed. The resultant estimators retain the convergence rates of the previous estimators and a Monte Carlo finite sample study reveals that they perform well relative to the ones based on the optimal truncation parameter.     

原料混匀料车间的设计优化与仿真(英文)

提出了基于参数化建模与虚拟现实技术的钢铁厂原料混匀系统的设计优化与仿真系统的结构。研究了设备设计优化、车间参数化建模和原料输送仿真技术,在此基础上,开发了混匀料车间设计与仿真的集成自动化系统。应用表明,该系统可提高原料混匀料车间的设计自动化水平。     

Asymptotic properties of randomly indexed sequences of random variables

Conditions are given for a randomly indexed sequence of random variables to converge weakly. The key concept employed is the so-called generalized Anscombe condition. The results give a method of determining sequential stopping rules, which have the required accuracy of estimation of an unknown parameter in the case when the observations are not necessarily independent and identically distributed.     

Penalized minimum-distance estimates in finite mixture models

When finite mixture models are used to fit data, it is sometimes important to estimate the number of mixture components. A nonparametric maximum-likelihood approach may result in too many support points and, in general, does not yield a consistent estimator. A penalized likelihood approach tends to produce a fit with fewer components, but it is not known whether that approach produces a consistent estimate of the number of mixture components. We suggest the use of a penalized minimum-distance method. It is shown that the estimator obtained is consistent for both the mixing distribution and the number of mixture components.     

Estimation of General Stationary Processes by Variable Length Markov Chains

We develop new results about a sieve methodology for the estimation of minimal state spaces and probability laws in the class of stationary processes defined on finite categorical spaces. Using a sieve approximation with variable length Markov chains of increasing order, we show that an adapted version of the Context algorithm yields asymptotically correct estimates for the minimal state space and for the underlying probability distribution. As a side product, the method of sieves yields a nice graphical tree representation for the potentially infinite dimensional minimal state space of the data generating process, which is very useful for exploration of the memory.     

快速可编程SAW跳频频率合成器

从理论上阐述了采用注入选模法实现多模声表面波振荡器（ＳＡＷＯ）的频率选择，控制频率合成器的输出，实现跳频的机理，并得到了实验证实。该方法简单易行、方便灵活，可以便跳频转换时间（ＴｒａｎｓｉｅｎｔＴｉｍｅ）低于２μｓ，振荡器输出频率信号对多模ＳＡＷＯ其它模式频率信号的抑制能力优于＋６０ｄＢ，并具有很高的频谱纯度。     

Estimating conditional occupation-time distributions for dependent sequences

Consider a random integer-valued process X(t) on Z+ that satisfies some weak dependence condition. We study the empirical distribution function of the occupation times of such a process and prove convergence to a suitable Gaussian process. An application to the statistical analysis of open and closed sojourn-time distributions for ion channels is provided.