D-efficient window experimental designs

In the paper we present a new method of calculating sampling intervals, so-called windows, allowing an experimenter some flexibility in timing the sample collection, while a minimum required design efficiency for parameter estimation is assured. The method is based on the Equivalence Theorem for D-optimality what makes the length of each window related to the parameter sensitivities. An example of calculating the windows in a pharmacokinetic study is presented. Some other methods of calculating efficient sampling windows are briefly discussed.     

含修正项的KdV方程是否一定存在孤子尾

含修正项的KdV方程是否一定存在孤子尾是最近孤子理论中争论的中心问题之一。给出几类含修正项的KdV方程的精确解 ,证明含修正项的KdV方程不一定存在孤子尾     

The evaluation of DNA evidence in pedigrees requiring population inference

Summary. The evaluation of nuclear DNA evidence for identification purposes is performed here taking account of the uncertainty about population parameters. Graphical models are used to detail the hypotheses being debated in a trial with the aim of obtaining a directed acyclic graph. Graphs also clarify the set of evidence that contributes to population inferences and they also describe the conditional independence structure of DNA evidence. Numerical illustrations are provided by re-examining three case-studies taken from the literature. Our calculations of the weight of evidence differ from those given by the authors of case-studies in that they reveal more conservative values.     

论南京国民政府审计立法规范体系的结构特征

注重对政府审计规范体系的构建 ,是南京国民政府审计制度建设的重要特征之一 ,也是政府审计执行财政上的司法监督的法律依据。南京国民政府统治时期 ,曾致力于创建政府审计制度 ,对政府审计进行一系列的立法 ,构建起了一个比较成型的审计规范体系框架 ;该审计规范体系有四个明显的特征 ,即鲜明的层次性、二维的互补性、兼容的承袭性、相对的滞后性。     

Banach空间中非线性脉冲Volterra积分方程解集的紧性与连通性

研究了Banach空间中的非线性脉冲Volterra积分方程的Lploc解的紧性与连通性.     

几类积分微分方程的可积类型

本文借助求导,迭代等方法,给出几类新的积分微分方程,并给出相应的通积分公式。并举例说明其在实际解题中的应用。     

上犹社溪方言实词的兼类与活用

当语言中的一个新词诞生以后 ,只要表示与之相关的动作变化或状态的动词或形容词没有产生 ,就很可能直接用这个词来表示这种相关的动作变化或状态。因此 ,词的兼类是各种语言或方言、或各个不同时期的同一语言中常见的现象。在语言的发展史上 ,尽可能减少词的兼类现象 ,特别是通过形态变化等手段把兼类的词分化为不同的词 ,还有些词存在活用现象 ,显得很有地方特色。从此角度说 ,客家方言保留着较多的古语痕迹。     

A spatiotemporal auto-regressive moving average model for solar radiation

Summary.  To investigate the variability in energy output from a network of photovoltaic cells, solar radiation was recorded at 10 sites every 10 min in the Pentland Hills to the south of Edinburgh. We identify spatiotemporal auto-regressive moving average models as the most appropriate to address this problem. Although previously considered computationally prohibitive to work with, we show that by approximating using toroidal space and fitting by matching auto-correlations, calculations can be substantially reduced. We find that a first-order spatiotemporal auto-regressive (STAR(1)) process with a first-order neighbourhood structure and a Matern noise process provide an adequate fit to the data, and we demonstrate its use in simulating realizations of energy output.     

Estimation of dependence between paired correlated failure times in the presence of covariate measurement error

Summary. In many biomedical studies, covariates are subject to measurement error. Although it is well known that the regression coefficients estimators can be substantially biased if the measurement error is not accommodated, there has been little study of the effect of covariate measurement error on the estimation of the dependence between bivariate failure times. We show that the dependence parameter estimator in the Clayton–Oakes model can be considerably biased if the measurement error in the covariate is not accommodated. In contrast with the typical bias towards the null for marginal regression coefficients, the dependence parameter can be biased in either direction. We introduce a bias reduction technique for the bivariate survival function in copula models while assuming an additive measurement error model and replicated measurement for the covariates, and we study the large and small sample properties of the dependence parameter estimator proposed.     

Conditional Likelihood Estimators for Hidden Markov Models and Stochastic Volatility Models

ABSTRACT.  This paper develops a new contrast process for parametric inference of general hidden Markov models, when the hidden chain has a non-compact state space. This contrast is based on the conditional likelihood approach, often used for ARCH-type models. We prove the strong consistency of the conditional likelihood estimators under appropriate conditions. The method is applied to the Kalman filter (for which this contrast and the exact likelihood lead to asymptotically equivalent estimators) and to the discretely observed stochastic volatility models.