Continuous-Time Stochastic Processes with Cyclical Long-Range Dependence

This paper introduces continuous‐time random processes whose spectral density is unbounded at some non‐zero frequencies. The discretized versions of these processes have asymptotic properties similar to those of discrete‐time Gegenbauer processes. The paper presents some properties of the covariance function and spectral density as well as a theory of statistical estimation of the mean and covariance function of such processes. Some directions for further generalizations of the results are indicated.     

风险投资公司业务流程评价及标杆管理的应用

考虑到风险投资公司知识型组织的特性及其业务流程的独特之处 ,对风险投资公司的业务流程进行评价时 ,主要针对的是知识管理流程 ,在设计具体指标时 ,除了考虑业务流程的一般度量指标外 ,还要从知识管理的角度来设置     

Testing for a trend in exponential means: A two-moment approximation to the null distribution of the likelihood-ratio statistic

The likelihood-ratio test statistic for testing homogeneity of exponential means with an ordered alternative has a rather complex null distribution. Expressions for the mean and variance of its null distribution are derived, and the accuracy of a two-moment chi-squared approximation is studied. The coefficients needed to implement the approximation are tabled. The application of these results in testing for a constant versus a nondecreasing intensity in a nonhomogeneous Poisson process is also discussed.     

A simple monotone process with application to radiocarbon-dated depth chronologies

Summary.  We propose a new and simple continuous Markov monotone stochastic process and use it to make inference on a partially observed monotone stochastic process. The process is piecewise linear, based on additive independent gamma increments arriving in a Poisson fashion. An independent increments variation allows very simple conditional simulation of sample paths given known values of the process. We take advantage of a reparameterization involving the Tweedie distribution to provide efficient computation. The motivating problem is the establishment of a chronology for samples taken from lake sediment cores, i.e. the attribution of a set of dates to samples of the core given their depths, knowing that the age–depth relationship is monotone. The chronological information arises from radiocarbon (¹⁴C) dating at a subset of depths. We use the process to model the stochastically varying rate of sedimentation.     

英语学困生阅读障碍的语音成因

英语学困生英文阅读障碍一直是英语教学与研究关注的重要课题。本文通过理论分析和综述,肯定了个体英语语音经验和技能与阅读成绩存在着显著的依存关系,主张英语学困生阅读障碍与语音缺陷有着必然的联系,并认为探索英文阅读障碍的语音成因具有普遍意义。     

The role of social support when giving up drug abuse: a female perspective

This article contains a presentation and analysis of the results from two qualitative studies, which examined female drug abusers' everyday lives and the social support available to them within their close relationships. Both studies concerned women who were participating in work-training or treatment programmes and who were in the process of giving up drug abuse. The two main questions concerned the supply of social support from the women's social networks and whether the amount of support available or lack of support had any impact on the exit process. Social support within different relationships of the women's social networks is presented, as well as different types of professional social support and treatment. The results in relation to previous research about female drug abusers and social support from gender and class perspectives are discussed. Conclusions about three important factors when creating a new life after drug abuse are drawn.     

A multistate approach for estimating the incidence of human immunodeficiency virus by using data from a prevalent cohort study

Summary.  Cohort studies of individuals infected with the human immunodeficiency virus (HIV) provide useful information on the past pattern of HIV diagnoses, progression of the disease and use of antiretroviral therapy. We propose a new method for using individual data from an open prevalent cohort study to estimate the incidence of HIV, by jointly modelling the HIV diagnosis, the inclusion in the cohort and the progression of the disease in a Markov model framework. The estimation procedure involves the construction of a likelihood function which takes into account the probability of observing the total number of subjects who are enrolled in the cohort and the probabilities of passage through the stages of disease for each observed subject conditionally on being included in the cohort. The estimator of the HIV infection rate is defined as the function which maximizes a penalized likelihood, and the solution of this maximization problem is approximated on a basis of cubic M -splines. The method is illustrated by using cohort data from a hospital-based surveillance system of HIV infection in Aquitaine, a region of south-western France. A simulation study is performed to study the ability of the model to reconstruct the incidence of HIV from prevalent cohort data.     

Asymptotic posterior normality for multiparameter problems

For asymptotic posterior normality in the one-parameter cases, Weng [2003. On Stein's identity for posterior normality. Statist. Sinica 13, 495–506] proposed to use a version of Stein's Identity to write the posterior expectations for functions of a normalized quantity in a form that is more transparent and can be easily analyzed. In the present paper we extend this approach to the multi-parameter cases and compare our conditions with earlier work. Three examples are used to illustrate the application of this method.     

试论中韩语言接触及中韩音韵关系

汉字及其音和义在三千年前就传到了韩半岛,特别是朝鲜王朝时期,世宗大王及学者深入研究汉语音韵,从而创造出自己的语言文字——训民正音。中韩语言的比较研究及中韩音韵关系的研究对语言学的理论发展是有价值的。     

Accounting for Both Random Errors and Systematic Errors in Uncertainty Propagation Analysis of Computer Models Involving Experimental Measurements with Monte Carlo Methods

A Monte Carlo method is presented to study the effect of systematic and random errors on computer models mainly dealing with experimental data. It is a common assumption in this type of models (linear and nonlinear regression, and nonregression computer models) involving experimental measurements that the error sources are mainly random and independent with no constant background errors (systematic errors). However, from comparisons of different experimental data sources evidence is often found of significant bias or calibration errors. The uncertainty analysis approach presented in this work is based on the analysis of cumulative probability distributions for output variables of the models involved taking into account the effect of both types of errors. The probability distributions are obtained by performing Monte Carlo simulation coupled with appropriate definitions for the random and systematic errors. The main objectives are to detect the error source with stochastic dominance on the uncertainty propagation and the combined effect on output variables of the models. The results from the case studies analyzed show that the approach is able to distinguish which error type has a more significant effect on the performance of the model. Also, it was found that systematic or calibration errors, if present, cannot be neglected in uncertainty analysis of models dependent on experimental measurements such as chemical and physical properties. The approach can be used to facilitate decision making in fields related to safety factors selection, modeling, experimental data measurement, and experimental design.