认知、语言交际与译者心智模型

翻译的过程是人类所经历并体验的复杂心智过程,涉及到社会文化、心理认知、情绪取向和意识状态等众多因素。从心智模型的视角来研究翻译过程,是认知科学的研究成果“心智模型”应用于翻译研究的一个全新且有生命力的尝试。翻译过程中的意义传送必定通过译者心智模型密切地关联和过滤。译者的心智模型在很大程度上助益或影响了译者对于原作语言形式的体验及其意义的表征推理以及其在目的语中意义及语言的选择和表述,从而决定了译文的语言风格及意义色彩。     

Two-stage DEA models with undesirable input-intermediate-outputs

Data envelopment analysis (DEA) is a non-parametric approach for measuring the relative efficiencies of peer decision making units (DMUs). Many studies have examined DEA efficiencies of two-stage systems, where all the outputs from the first stage are the only inputs to the second stage. Although single-stage DEA models with undesirable input-outputs have been extensively studied, there still lacks of more systematical investigation on two-stage DEA with undesirable variables. For instance, depending on its operating model, even whether an intermediate variable is desirable or undesirable can be questionable for a particular two-stage system. Furthermore, most of the existing studies on two-stage systems focus on the case where only the final outputs are undesirable. In this work, we try to systematically examine two-stage DEA models with undesirable input-intermediate-outputs. Particularly, we utilize the free-disposal axioms to construct the production possibility sets (PPS) and the corresponding DEA models with undesirable variables. The proposed models are then used to illustrate some theoretical perspectives by using the data of China׳s listed banks.     

Empirical likelihood for generalized linear models with fixed and adaptive designs

Empirical likelihood inference for generalized linear models with fixed and adaptive designs is considered. It is shown that the empirical log-likelihood ratio at the true parameters converges to the standard chi-square distribution. Furthermore, we obtain the maximum empirical likelihood estimate of the unknown parameter and the resulting estimator is shown to be asymptotically normal. Some simulations are conducted to illustrate the proposed method.     

Locally and maximin optimal designs for multi-factor nonlinear models

This paper considers the search for locally and maximin optimal designs for multi-factor nonlinear models from optimal designs for sub-models of a lower dimension. In particular, sufficient conditions are given so that maximin D-optimal designs for additive multi-factor nonlinear models can be built from maximin D-optimal designs for their sub-models with a single factor. Some examples of application are models involving exponential decay in several variables.     

Cointegrated VARIMA Models: Specification and Simulation

In this note, we show how specify cointegrated vector autoregressive-moving average models and how they can be used to generate cointegrated time series.     

Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data

The paper describes the use of frequentist and Bayesian shared-parameter joint models of longitudinal measurements of prostate-specific antigen (PSA) and the risk of prostate cancer (PCa). The motivating dataset corresponds to the screening arm of the Spanish branch of the European Randomized Screening for Prostate Cancer study. The results show that PSA is highly associated with the risk of being diagnosed with PCa and that there is an age-varying effect of PSA on PCa risk. Both the frequentist and Bayesian paradigms produced very close parameter estimates and subsequent 95% confidence and credibility intervals. Dynamic estimations of disease-free probabilities obtained using Bayesian inference highlight the potential of joint models to guide personalized risk-based screening strategies.     

A spatio-temporal model for assessing winter damage risk to east coast vineyards

Climate is an essential component in site suitability for agriculture in general, and specifically in viticulture. With the recent increase in vineyards on the East Coast, an important climactic consideration in site suitability is extreme winter temperature. Often, maps of annual minimum temperatures are used to determine cold hardiness. However, cold hardiness of grapes is a more complicated process, since the temperature that grapes are able to withstand without damage is not constant. Rather, recent temperature cause acclimation or deacclimation and hence, have a large influence on cold hardiness. By combining National Oceanic and Atmospheric Administration (NOAA) weather station data and leveraging recently created cold hardiness models for grapes, we develop a dynamic spatio-temporal model to determine the risk of winter damage due to extreme cold for several grape varieties commonly grown in the eastern United States. This analysis provides maps of winter damage risk to three grape varieties, Chardonnay, Cabernet Sauvignon, and Concord.     

Outlier Resistant Estimation in Difference-Based Semiparametric Partially Linear Models

Partially linear models are extensions of linear models that include a nonparametric function of some covariate allowing an adequate and more flexible handling of explanatory variables than in linear models. The difference-based estimation in partially linear models is an approach designed to estimate parametric component by using the ordinary least squares estimator after removing the nonparametric component from the model by differencing. However, it is known that least squares estimates do not provide useful information for the majority of data when the error distribution is not normal, particularly when the errors are heavy-tailed and when outliers are present in the dataset. This paper aims to find an outlier-resistant fit that represents the information in the majority of the data by robustly estimating the parametric and the nonparametric components of the partially linear model. Simulations and a real data example are used to illustrate the feasibility of the proposed methodology and to compare it with the classical difference-based estimator when outliers exist.