英语中名词性间接回指的认知语义解析

根据兰盖克的认知语法,英语中名词性间接回指的语义本质是基体—侧重有机结合体。名词性间接回指含有两个基本构成元素,即先行项和名词性回指项,但二者不共指。回指项的语义预设了先行项的意义,先行项的语义就成为名词性间接回指的基体。分析不同类型的名词性间接回指的语义特征,并指出名词性间接回指的语义是存在于认知主体大脑中的心理实体,它必然受到认知主体的认知域的限制,所以名词性间接回指的解读存在一定的个体差异性。     

Testing Treatment-Covariate Interaction in a Nonparametric Analysis of Covariance Model

We consider a recently introduced nonparametric model for Analysis of Covariance and derive an asymptotic test for interaction between covariate and treatment. Furthermore, we suggest data depth techniques to obtain joint confidence regions for the covariate effects in this model.

The finite sample behavior of the asymptotic method is evaluated in simulations. Application of the procedures is illustrated using an epileptic seizures and chemotherapy data set.     

On the accuracy of the bootstrap approximation of the joint distribution of sample quantiles

It is proved that the accuracy of the bootstrap approximation of the joint distribution of sample quantiles lies between O(n^?1/4) and O(n^?1/4 a_n), where (log(n))1/2=O(a_n). As an application, we investigated confidence intervals based on the bootstrap.     

On the simple step-stress model for two-parameter exponential distribution

In this paper, we consider the simple step-stress model for a two-parameter exponential distribution, when both the parameters are unknown and the data are Type-II censored. It is assumed that under two different stress levels, the scale parameter only changes but the location parameter remains unchanged. It is observed that the maximum likelihood estimators do not always exist. We obtain the maximum likelihood estimates of the unknown parameters whenever they exist. We provide the exact conditional distributions of the maximum likelihood estimators of the scale parameters. Since the construction of the exact confidence intervals is very difficult from the conditional distributions, we propose to use the observed Fisher Information matrix for this purpose. We have suggested to use the bootstrap method for constructing confidence intervals. Bayes estimates and associated credible intervals are obtained using the importance sampling technique. Extensive simulations are performed to compare the performances of the different confidence and credible intervals in terms of their coverage percentages and average lengths. The performances of the bootstrap confidence intervals are quite satisfactory even for small sample sizes.     

Empirical likelihood confidence bands for distribution functions with missing responses

Distribution function estimation plays a significant role of foundation in statistics since the population distribution is always involved in statistical inference and is usually unknown. In this paper, we consider the estimation of the distribution function of a response variable Y with missing responses in the regression problems. It is proved that the augmented inverse probability weighted estimator converges weakly to a zero mean Gaussian process. A augmented inverse probability weighted empirical log-likelihood function is also defined. It is shown that the empirical log-likelihood converges weakly to the square of a Gaussian process with mean zero and variance one. We apply these results to the construction of Gaussian process approximation based confidence bands and empirical likelihood based confidence bands of the distribution function of Y. A simulation is conducted to evaluate the confidence bands.     

Inferences on the lognormal mean for complete samples

In many engineering problems it is necessary to draw statistical inferences on the mean of a lognormal distribution based on a complete sample of observations. Statistical demonstration of mean time to repair (MTTR) is one example. Although optimum confidence intervals and hypothesis tests for the lognormal mean have been developed, they are difficult to use, requiring extensive tables and/or a computer. In this paper, simplified conservative methods for calculating confidence intervals or hypothesis tests for the lognormal mean are presented. In this paper, “conservative” refers to confidence intervals (hypothesis tests) whose infimum coverage probability (supremum probability of rejecting the null hypothesis taken over parameter values under the null hypothesis) equals the nominal level. The term “conservative” has obvious implications to confidence intervals (they are “wider” in some sense than their optimum or exact counterparts). Applying the term “conservative” to hypothesis tests should not be confusing if it is remembered that this implies that their equivalent confidence intervals are conservative. No implication of optimality is intended for these conservative procedures. It is emphasized that these are direct statistical inference methods for the lognormal mean, as opposed to the already well-known methods for the parameters of the underlying normal distribution. The method currently employed in MIL-STD-471A for statistical demonstration of MTTR is analyzed and compared to the new method in terms of asymptotic relative efficiency. The new methods are also compared to the optimum methods derived by Land (1971, 1973).     

Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data

Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine‐tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross‐validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.     

双重的约束：女性科技人才成长的制约因素分析

近年来，我国的科技事业发展迅速。然而，科技人才队伍中的女性科技人才却发展较为缓慢，原因在于女性科技人才的发展受到了公私领域的双重约束即私人领域的家庭劳动、家庭决策、家庭教育投资以及公共领域的科技人才准入、培育和评价机制制约了女性科技人才的发展。在这种双重约束下，女性科技人才只能在具有性别排斥的科技领域中充当幕后支撑力量，相反，男性则在科技人才发展中占主导优势，成为主要的受惠群体。     

New High-Speed Computing Devices

“One method of error analysis (not the one we will use) is based upon the principles of mathematical statistics. Unfortunately, statistical methods can only be meaningfully applied when one has large amounts of data for a given system. In many cases … these large quantities of data are not available … then statistical methods are not applicable, and some other methods must be devised.”     

集群式经济:我国县域经济发展的新思路--兼论我国县域经济发展的几个认识误区

通过分析县域经济发展的重要性,指出其中存在的理论与实践误区,提出走集群式经济的发展思路,最终指出要把培养集群式经济作为县城经济发展的突破口.