认知语用学中的翻译观

本文对传统翻译活动进行了回顾,指出其理论基础的不足,在此基础上,从认知语用学的角度,对翻译的理解与表达活动进行了新的阐述,认为翻译理解实际上是译者对自身认知语境的搜索过程,翻译的表达是基于对读者认知语境的预测的交际策略的选择,并受原作者认知语境的制约.本文对翻译中的基本问题如直译或意译问题也作了探讨.     

一种新的非线性模式分类器方法

根据模糊推理理论，提出了建立模式分类模糊决策规则的方法，该方法可适于各种非线性决策面的模式分类。通过仿真试验证明了该方法的有效性。     

Frequentist and Bayesian approaches for interval-censored data

Interval censoring appears when the event of interest is only known to have occurred within a random time interval. Estimation and hypothesis testing procedures for interval-censored data are surveyed. We distinguish between frequentist and Bayesian approaches. Computational aspects for every proposed method are described and solutions with S-Plus, whenever are feasible, are mentioned. Three real data sets are analyzed.     

Stopping-time resampling for sequential Monte Carlo methods

Summary.  Motivated by the statistical inference problem in population genetics, we present a new sequential importance sampling with resampling strategy. The idea of resampling is key to the recent surge of popularity of sequential Monte Carlo methods in the statistics and engin-eering communities, but existing resampling techniques do not work well for coalescent-based inference problems in population genetics. We develop a new method called 'stopping-time resampling', which allows us to compare partially simulated samples at different stages to terminate unpromising partial samples and to multiply promising samples early on. To illustrate the idea, we first apply the new method to approximate the solution of a Dirichlet problem and the likelihood function of a non-Markovian process. Then we focus on its application in population genetics. All our examples show that the new resampling method can significantly improve the computational efficiency of existing sequential importance sampling methods.     

齿轮系统非线性动力学研究进展及展望

基于非线性系统的分析方法，综述了齿轮系统的非线性动力学研究概况，包括基于解析方法，数值方法，实验方法，以及考虑齿面摩擦和齿面故障的齿轮系统的非线性动力学研究。最后指出了若干可能的研究热点和方向，如带故障的齿轮系统非线性动力学行为研究，齿轮系统的振动控制研究，以及齿轮系统非线性动力学中的反问题研究等。     

VARIANCE ESTIMATION WITH HIGHLY STRATIFIED SAMPLING DESIGNS WITH UNEQUAL PROBABILITIES

It is common practice to design a survey with a large number of strata. However, in this case the usual techniques for variance estimation can be inaccurate. This paper proposes a variance estimator for estimators of totals. The method proposed can be implemented with standard statistical packages without any specific programming, as it involves simple techniques of estimation, such as regression fitting.     

Bayesian correlated factor analysis of socio-demographic indicators

Recent changes in European family dynamics are often linked to common latent trends of economic and ideational change. Using Bayesian factor analysis, we extract three latent variables from eight socio-demographic indicators related to family formation, dissolution, and gender system and collected on 19 European countries within four periods (1970, 1980, 1990, 1998). The flexibility of the Bayesian approach allows us to introduce an innovative temporal factor model, adding the temporal dimension to the traditional factorial analysis. The underlying structure of the Bayesian factor model proposed reflects our idea of an autoregressive pattern in the latent variables relative to adjacent time periods. The results we obtain are consistent with current interpretations in European demographic trends.     

BAYESIAN ANALYSIS OF VECTOR ARFIMA PROCESSES

A general framework is presented for Bayesian inference of multivariate time series exhibiting long-range dependence. The series are modelled using a vector autoregressive fractionally integrated moving-average (VARFIMA) process, which can capture both short-term correlation structure and long-range dependence characteristics of the individual series, as well as interdependence and feedback relationships between the series. To facilitate a sampling-based Bayesian approach, the exact joint posterior density is derived for the parameters, in a form that is computationally simpler than direct evaluation of the likelihood, and a modified Gibbs sampling algorithm is used to generate samples from the complete conditional distribution associated with each parameter. The paper also shows how an approximate form of the joint posterior density may be used for long time series. The procedure is illustrated using sea surface temperatures measured at three locations along the central California coast. These series are believed to be interdependent due to similarities in local atmospheric conditions at the different locations, and previous studies have found that they exhibit ‘long memory’ when studied individually. The approach adopted here permits investigation of the effects on model estimation of the interdependence and feedback relationships between the series.     

企业集团内部绩效评价的综合分析模型

针对当前我国企业集团管理方法研究中的不足 ,以我国企业集团的现实情况为背景 ,提出一种企业集团进行战略性分析的内部绩效评价方法—九方格综合分析模型。其评价不仅基于现行业绩 ,且基于未来发展潜力 ,两方面的评价均采用定量分析 ;综合评价采用九方格评价屏幕 ,可清楚直观地展示集团成员的实力分布情况 ,便于分析和应用。     

Exact Non-Parametric Confidence, Prediction and Tolerance Intervals with Progressive Type-II Censoring

Abstract.  This article extends recent results [Scand. J. Statist. 28 (2001) 699] about exact non-parametric inferences based on order statistics with progressive type-II censoring. The extension lies in that non-parametric inferences are now covered where the dependence between involved order statistics cannot be circumvented. These inferences include: (a) tolerance intervals containing at least a specified proportion of the parent distribution, (b) prediction intervals containing at least a specified number of observations in a future sample, and (c) outer and/or inner confidence intervals for a quantile interval of the parent distribution. The inferences are valid for any parent distribution with continuous distribution function. The key result shows how the probability of an event involving k dependent order statistics that are observable/uncensored with progressive type-II censoring can be represented as a mixture with known weights of corresponding probabilities involving k dependent ordinary order statistics. Further applications/developments concerning exact Kolmogorov-type confidence regions are indicated.