拟线性抛物型方程在第三边值条件下的极大值原理

文章以线性抛物型方程的弱极值原理和强极值原理为主要工具,讨论了拟线性抛物型方程β（tu）=Δu＋（fx,t,u）解的泛函V（x,）t=g（u）ut＋h（u）和β（tu）=Δu＋（fu）解的含梯度泛函P（x,t,u,u）k=塄u2＋2Z（）tF（u）（F＇=）f,在第三边值条件下的极大值原理。     

一类带有Newman边界条件的非线性抛物方程的解的爆破和整体存在

通过构造辅助函数和利用最大值原理,得到了非线性抛物方程(g(u))t=▽.(a(u)b(x)c(t)▽u)+h(t)f(u)的解的爆破和整体存在的充分条件,给出了爆破时间的上界和爆破速率的上估计以及整体存在解的上估计.     

最大子图序列平滑的飞控图层无缝融合技术

多图层无缝融合技术是三维视景仿真中的难点,无缝融合中要求对多个视景图层的融合点进行有效平滑,达到图层深度嵌入的目的。传统的三维飞控图层无缝融合方法采用基于RGB颜色模糊调配与像素点函数内插方法实现,以高阶奇次曲线拟合为目标函数,内插形成图层融合过渡带,此方法对于图层差异较小的融合有较好效果,当图层差异较大时,效果不佳。提出一种基于最大子图序列平滑的三维飞控图层无缝融合技术,对不同图层采用快闪波门预测,扣取图层的最大子图序列,然后将融合图层的最大子图序列放入平滑器进行平滑,采用序列平滑校正方法,对偏离平滑曲线的点进行校正,形成无缝图层融合平滑输出。采用实际三维飞控视景仿真图层进行实验,结果显示,采用该方法,图层融合效果明显优于传统方法,在三维视景仿真的图层融合中具有很好的应用价值。     

基于信息熵理论的随机变量统计模型

根据信息熵理论构建出最大熵概率密度函数模型,得到最少偏见的随机变量的概率分布。在MATLAB平台上编制最大熵概率密度函数的程序。用最大熵概率密度函数的方法对已知的一些标准分布再现,结果表明,基于最大熵概念上的随机变量统计模型是可行、有效的。     

关于中文分词问题数据结构模型的讨论

在分词工作常用数据结构模型的基础上,提出了字符串完全分词网络模型,讨论了该模型的基本性质,给出了其中的路径查找基本算法。该模型能反映中文分词问题自身的特点,便于分析分词问题中的统一性质。利用该模型将全切分图的生成、修改、路径查找等工作分解为统一平台上相对独立的过程,能较好地配合多种常用分词算法,简化对各种算法的研究和描述。该模型与自然语言理解后续工作使用的数据结构如句法树等也有很好的相似性。     

Strong Consistency of Reliability Estimators for Multiple-Component Measuring Instruments

It is shown that the maximum likelihood estimator of the widely used omega coefficient for reliability of multicomponent measuring instruments converges almost surely to the population reliability coefficient for normal congeneric measures with uncorrelated errors as sample size increases indefinitely. This strong consistency implies convergence in probability (consistency) as well as in distribution for the omega estimator. Strong consistency is also demonstrated for the maximal reliability estimator associated with the optimal linear combination of the instrument components. The findings of this note add (i) to the recommendation to use in the general normality case the omega estimator in empirical research, (ii) to the critical literature on the popular coefficient alpha then, and (iii) to the literature on the properties of the optimal linear combination of observed measures and the maximal reliability estimator.     

Using SEM to Analyze Complex Survey Data: A Comparison between Design-Based Single-Level and Model-Based Multilevel Approaches

Both ad-hoc robust sandwich standard error estimators (design-based approach) and multilevel analysis (model-based approach) are commonly used for analyzing complex survey data with nonindependent observations. Although these 2 approaches perform equally well on analyzing complex survey data with equal between- and within-level model structures (B. O. Muthén & Satorra, 1995), the performances of these 2 approaches for analyzing multilevel data with unequal between- and within-level structures have not yet been systematically examined. In this study, we extended B. O. Muthén and Satorra's (1995) Muthén, B. O. and Satorra, A. 1995. Complex sample data in structural equation modeling. Sociological Methodology, 25: 267–316. [Crossref], [Web of Science ®] , [Google Scholar] study by comparing these 2 approaches and an additional model-based maximum model for analyzing multilevel data considering number of clusters, cluster size, intraclass correlation, and the equality of different level structures. The simulation results showed the model-based maximum model generally performed well across conditions. This model is also recommended as an alternative for analyzing nonindependent survey data, especially when the information of the higher level model structure is not known.     

Maximum Likelihood Dynamic Factor Modeling for Arbitrary N and T Using SEM

This article has 3 objectives that build on each other. First, we demonstrate how to obtain maximum likelihood estimates for dynamic factor models (the direct autoregressive factor score model) with arbitrary T and N by means of structural equation modeling (SEM) and compare the approach to existing methods. Second, we go beyond standard time series analysis (T large and N = 1) and conventional SEM (N large and T = 1 or small) by integrating both approaches. The resulting combined model offers a variety of new modeling options including a direct test of the ergodicity hypothesis, according to which the factorial structure of an individual observed at many time points is identical to the factorial structure of a group of individuals observed at a single point in time. Third, we illustrate the flexibility of SEM time series modeling by extending the approach to account for complex error structures. We end with a discussion of current limitations and future applications of SEM-based time series modeling for arbitrary T and N.     

Using Instrumental Variables to Estimate the Parameters in Unconditional and Conditional Second-Order Latent Growth Models

This article applies Bollen’s (1996) 2-stage least squares/instrumental variables (2SLS/IV) approach for estimating the parameters in an unconditional and a conditional second-order latent growth model (LGM). First, the 2SLS/IV approach for the estimation of the means and the path coefficients in a second-order LGM is derived. An empirical example is then used to show that 2SLS/IV yields estimates that are similar to maximum likelihood (ML) in the estimation of a conditional second-order LGM. Three subsequent simulation studies are then presented to show that the new approach is as accurate as ML and that it is more robust against misspecifications of the growth trajectory than ML. Together, these results suggest that 2SLS/IV should be considered as an alternative to the commonly applied ML estimator.     

对数正态分布参数的最大似然估计

利用最大似然估计法求出了对数正态分布两个参数的估计量，并讨论了它们的无偏性和相合性。