非连续型Volterra积分方程最小和最大解

本文研究了带有不连续项的 Volterra积分方程的最小和最大解 ,并证明了参考文献 [1]中的主要结果的条件可被较弱条件代替     

Semiparametric Likelihood Based Method for Goodness of Fit Tests and Estimation in Upgraded Mixture Models

We use Owen's (1988, 1990) empirical likelihood method in upgraded mixture models. Two groups of independent observations are available. One is z ₁, ..., z _n which is observed directly from a distribution F ( z ). The other one is x ₁, ..., x _m which is observed indirectly from F ( z ), where the x _i s have density ∫ p ( x | z ) dF ( z ) and p ( x | z ) is a conditional density function. We are interested in testing H ₀: p ( x | z ) = p ( x | z ; θ ), for some specified smooth density function. A semiparametric likelihood ratio based statistic is proposed and it is shown that it converges to a chi-squared distribution. This is a simple method for doing goodness of fit tests, especially when x is a discrete variable with finitely many values. In addition, we discuss estimation of θ and F ( z ) when H ₀ is true. The connection between upgraded mixture models and general estimating equations is pointed out.     

混流式水轮机转轮内部流场数值分析

从Ｎａｖｉｅｒ－Ｓｔｏｋｅｓ方程出发，采用层流模型描述水流动状态，并在欧拉坐标系中用速度－压力修正法对模型进行求解，考虑到转轮内流道几何形状比较复杂，故采用有限元法进行数值离散，并将该解法用ＦＯＲＴＲＡＮ语言编制计算程序，对三峡工程混 式水轮机转轮内部的流场进行数值计算，分析了转轮内的速度和压力分布。     

Quantile Regression Methods for Longitudinal Data with Drop-outs: Application to CD4 Cell Counts of Patients Infected with the Human Immunodeficiency Virus

Patients infected with the human immunodeficiency virus (HIV) generally experience a decline in their CD4 cell count (a count of certain white blood cells). We describe the use of quantile regression methods to analyse longitudinal data on CD4 cell counts from 1300 patients who participated in clinical trials that compared two therapeutic treatments: zidovudine and didanosine. It is of scientific interest to determine any treatment differences in the CD4 cell counts over a short treatment period. However, the analysis of the CD4 data is complicated by drop-outs: patients with lower CD4 cell counts at the base-line appear more likely to drop out at later measurement occasions. Motivated by this example, we describe the use of `weighted' estimating equations in quantile regression models for longitudinal data with drop-outs. In particular, the conventional estimating equations for the quantile regression parameters are weighted inversely proportionally to the probability of drop-out. This approach requires the process generating the missing data to be estimable but makes no assumptions about the distribution of the responses other than those imposed by the quantile regression model. This method yields consistent estimates of the quantile regression parameters provided that the model for drop-out has been correctly specified. The methodology proposed is applied to the CD4 cell count data and the results are compared with those obtained from an `unweighted' analysis. These results demonstrate how an analysis that fails to account for drop-outs can mislead.     

化学热力学函数偏微商等式证明方法探讨

从基础热力学函数的相互关系出发，结合偏微商运算基本原理，归纳热力学函数偏微商变换的常用途径，并对化学热力学函数偏微商式的证明方法及技巧进行一些具体研讨。     

Alternate test statistics for a functional errors-in-variables model

Some test statistics for the structural coefficients of simultaneous equations model often referred to as the multivariate linear functional relationship model are proposed in this article. The following cases are considered: the covariance matrix of errors is either unknown, known up to a proportionality factor, or completely known. The exact and approximate distributions of the proposed test statistics, as well as those of some that are known, are also given.     

Seasonal Time Series and Transfer Function Modeling

A simple modification is suggested for the construction of transfer function models relating an output variable Y_t to an input variable X_t when the model for X_t contains operators that cancel out. In addition, the evaluation of transfer function models is discussed by comparing the forecasts with the actual observations.     

A new class of measurement-error models,with applications to dietary data

Measurement-error modelling occurs when one cannot observe a covariate, but instead has possibly replicated surrogate versions of this covariate measured with error. The vast majority of the literature in measurement-error modelling assumes (typically with good reason) that given the value of the true but unobserved (latent) covariate, the replicated surrogates are unbiased for latent covariate and conditionally independent. In the area of nutritional epidemiology, there is some evidence from biomarker studies that this simple conditional independence model may break down due to two causes: (a) systematic biases depending on a person's body mass index, and (b) an additional random component of bias, so that the error structure is the same as a one-way random-effects model. We investigate this problem in the context of (1) estimating distribution of usual nutrient intake, (2) estimating the correlation between a nutrient instrument and usual nutrient intake, and (3) estimating the true relative risk from an estimated relative risk using the error-prone covariate. While systematic bias due to body mass index appears to have little effect, the additional random effect in the variance structure is shown to have a potentially important effect on overall results, both on corrections for relative risk estimates and in estimating the distribution of usual nutrient intake. However, the effect of dietary measurement error on both factors is shown via examples to depend strongly on the data set being used. Indeed, one of our data sets suggests that dietary measurement error may be masking a strong risk of fat on breast cancer, while for a second data set this masking is not so clear. Until further understanding of dietary measurement is available, measurement-error corrections must be done on a study-specific basis, sensitivity analyses should be conducted, and even then results of nutritional epidemiology studies relating diet to disease risk should be interpreted cautiously.     

联立方程计量经济学模型的结构识别

本文提出了联立方程计量经济学模型结构识别的概念与方法。应用该方法识别模型与统计数据无关,并且与模型参数的具体数值也无关,对计量经济学建模与分析有实际意义.     

具有非线性时滞的双曲型微分方程解振动的充分条件

给出具有非线性时滞的双曲型微分方程定解问题２ｕｔ２＝ａ（ｔ）Δｕ＋ｓｉ＝１ａｉ（ｔ）Δｕ（ｘ，ｔ－ρｉ（ｔ））－ｆ（ｘ，ｔ，ｕ）－ｋｊ＝１ｇｊ（ｘ，ｔ，ｕ（ｘ，ｔ－σｊ）），ｕ＝０，（ｘ，ｔ）∈Ω×〔０，∞），其中（ｘ，ｔ）∈Ω×（０，∞）的解振动的几个充分条件．