Weighted bivariate logarithmic series distributions

In this paper, we are interested in the weighted distributions of a bivariate three parameter logarithmic series distribution studied by Kocherlakota and Kocherlakota (1990). The weighted versions of the model are derived with weight W(x,y) = x^[r] y^[s]. Explicit expressions for the probability mass function and probability generating functions are derived in the case r = s = l. The marginal and conditional distributions are derived in the general case. The maximum likelihood estimation of the parameters, in both two parameter and three parameter cases, is studied. A procedure for computer generation of bivariate data from a discrete distribution is described. This enables us to present two examples, in order to illustrate the methods developed, for finding the maximum likelihood estimates.     

Smoothing a Time Series by Segments of the Data Range

We consider the problem of estimating a trend with different amounts of smoothness for segments of a time series subjected to different variability regimes. We propose using an unobserved components model to consider the existence of at least two data segments. We first fix some desired percentages of smoothness for the trend segments and deduce the corresponding smoothing parameters involved. Once the size of each segment is chosen, the smoothing formulas here derived produce trend estimates for all segments with the desired smoothness as well as their corresponding estimated variances. Empirical examples from demography and economics illustrate our proposal.     

On Locating and Characterizing Parameter Variation by the Mosumsq Test Statistic

In modelling a system structure it is often important to test the constancy of the structural parameters. If the hypothesis of constant parameters is rejected we wish to characterize the parameter variation. In this paper we examine the usefulness of the MOSUMSQ test statistics for locating and characterizing the parameter variation in linear regression models. The means and the variances of the test statistics are given for non-random parameter variation. The observed path of the MOSUMSQ test statistics provides valuable knowledge about the time of the onset of the parameter change and whether it is instantaneous or gradual.     

Bayesian estimation of multidimensional item response theory model using gibbs sampling

We propose Bayesian parameter estimation in a multidimensional item response theory model using the Gibbs sampling algorithm. We apply this approach to dichotomous responses to a questionnaire on sleep quality. The analysis helps determine the underlying dimensions.     

Improved likelihood-based inference for the stationary AR(2) model

An improved likelihood-based method based on Fraser et al. (1999) is proposed in this paper to test the significance of the second lag of the stationary AR(2) model. Compared with the test proposed by Fan and Yao (2003) and the signed log-likelihood ratio test, the proposed method has remarkable accuracy. Simulation studies are performed to illustrate the accuracy of the proposed method. Application of the proposed method on historical data is presented to demonstrate the implementation of this method. Furthermore, the method can be extended to the general AR(p) model.     

Exclusion restrictions in instrumental variables equations

In this paper we consider two-stage estimators of parameters of a structural equation in a model with recursive exclusion restrictions on the instrumental variables equations. The estimations considered are simple OLS and GLS estimators after substitution of estimates of the systematic part of the IV equations for the endogenous variables. It is known in the literature that neither imposing the restrictions in the first stage nor ignoring them will in general be more efficient than the alternative. We introduce a class of mixed instrumental variables estimators (MIV) with these possibilities as special cases which yields an estimator which is not only more efficient than the two stage estimators considered in the literature but as efficient as an efficient system estimator like 3SLS.     

饱和粘土蠕变特性研究及参数确定

将两种弹 粘弹性模型（Burgers和H 2K模型）的一维蠕变方程，转换成三维状态下的应力 应变 时间关系，分别推导出常应力下两种模型的轴向应变随时间变化的方程。采用分级加载法进行室内长期单轴压缩蠕变试验，结合两种模型三维应力状态的轴向应变表达式，对试验结果进行拟合，得出相应模型的本构参数。试验结果表明：饱和粘土在新增荷载作用下2~3　d变形趋于稳定，Burgers模型和H 2K模型均能较好地模拟饱和粘土的衰减蠕变过程；而H 2K模型能更好地模拟土样在常应力作用下的蠕变特性。     

中国小说理论与批评的哲性因子

“依经论文”、“依道论文”在中国古代小说评论中是一个重要的角度和原则,这些论述都在强调小说的思想性即哲性因子。那么,这是否就意味着中国古代文化人对小说就不采取“依史论文”的视角和原则呢?当然不是。余虹先生以及持相同见解的先生的观点是有大量史料为基础的,我们之所以特别突出地强调中国古代小说理论与批评中的哲性因子,是意在与余虹先生的见解构成互补,以防止偏误和片面的认识。     

深亚微米MOS器件模型BSIM2及其参数提取

详细分析了适用于ＶＬＳＩ／ＵＬＳＩ设计的深亚微米ＢＳＩＭ２ＭＯＳ器件模型，并在深入讨论短沟道ＭＯＳ器件物理效应的基础上，对沟道长１μｍ栅氧化层厚度２５ｎｍ的ｎＭＯＳＦＥＴ进行了测试和ＢＳＩＭ２模型参数的提取，且对结果进行了分析和讨论。结果表明，ＢＳＩＭ２具有精确、参数易提取、计算速度快的特点，是ＶＬＳＩ／ＵＬＳＩ模拟设计的重要工具之一。     

泡沫塑料衬垫缓冲性能11参数模型及其参数识别

提出了 11参数性粘弹塑性模型 ,反映了泡沫塑料衬垫缓冲性能的特征 .