化工英文专利说明书的文体特点及翻译

本文通过实例从词法、句法角度对英文专利说明书的文体特征进行分析阐述,说明其特点、规律及翻译方法。     

可视化UML模型验证环境研究

针对非形式化的描述可能导致的不明确和不一致问题,提出并设计实现了一种基于OCL的、能对UML模型中类之间的关联及约束进行分析与验证的OUVE,使得在编写代码之前就能很好地验证类之间的关联关系,以及约束条件是否与实际的要求相符合,降低了由于不明确和不一致导致的设计、编程的错误以及所要付出的修改代价。     

Market channel selections by US nursery plant producers: a multivariate nonparametric fractional regression analysis

Availability of market channel alternatives has helped the growth of ornamental plant sales in the United States. To identify the factors affecting the choice and allocation of outputs to different market channels by nursery producers, we first use a mixture of experts model to select clusters of homogenous subpopulations of US nursery producers based on a 2009 National Nursery Survey. The impact of growers’ business characteristics on shares of sales to these channels was estimated using multivariate parametric and nonparametric fractional regression models. Specification tests indicated a nonparametric model was superior to a parametric model in some clusters. Important explanatory variables affecting the sales volume to different channels were sales of plant groups, kinds of contract sales, promotional expenses, and farm size. Results indicated the existence of clear market segmentation of nursery producers in the United States.     

高师音乐教师教育人才培养规格的基本设想

高师音乐教师教育改革在近几年来提出很多现实的音乐教育问题,而人才培养规格起着提纲挈领的作用。人才培养规格的制定是教育教学改革的灵魂,只有科学地、合理地制定人才培养规格,才能确定教育教学中一系列教学改革活动的方向,为课程设置、教学内容、教学大纲的制定提供依据。当代高师音乐教师教育的人才培养,首先应以就业市场为导向,使音乐教师培养与就业单位形成”适销对路”的良性供求状态;其次,以综合能力培养为基础,不断融入新的教育教学理念,建构音乐教师完备的知识体系;再次,加强音乐教师的教育特色,以教学能力培养为主线,加强音乐教师从教技能,并在课程设置、教学实践等方面进行改革,促进音乐教师教学能力的全面提高。     

Testing a Parametric Model Against a Nonparametric Alternative with Identification Through Instrumental Variables

This paper is concerned with inference about a function g that is identified by a conditional moment restriction involving instrumental variables. The paper presents a test of the hypothesis that g belongs to a finite‐dimensional parametric family against a nonparametric alternative. The test does not require nonparametric estimation of g and is not subject to the ill‐posed inverse problem of nonparametric instrumental variables estimation. Under mild conditions, the test is consistent against any alternative model. In large samples, its power is arbitrarily close to 1 uniformly over a class of alternatives whose distance from the null hypothesis is O(n^−1/2), where n is the sample size. In Monte Carlo simulations, the finite‐sample power of the new test exceeds that of existing tests.     

A New Specification Test for the Validity of Instrumental Variables

We develop a new specification test for IV estimators adopting a particular second order approximation of Bekker. The new specification test compares the difference of the forward (conventional) 2SLS estimator of the coefficient of the right‐hand side endogenous variable with the reverse 2SLS estimator of the same unknown parameter when the normalization is changed. Under the null hypothesis that conventional first order asymptotics provide a reliable guide to inference, the two estimates should be very similar. Our test sees whether the resulting difference in the two estimates satisfies the results of second order asymptotic theory. Essentially the same idea is applied to develop another new specification test using second‐order unbiased estimators of the type first proposed by Nagar. If the forward and reverse Nagar‐type estimators are not significantly different we recommend estimation by LIML, which we demonstrate is the optimal linear combination of the Nagar‐type estimators (to second order). We also demonstrate the high degree of similarity for k‐class estimators between the approach of Bekker and the Edgeworth expansion approach of Rothenberg. An empirical example and Monte Carlo evidence demonstrate the operation of the new specification test.     

Multiple imputation for ordinal longitudinal data with monotone missing data patterns

Missing data often complicate the analysis of scientific data. Multiple imputation is a general purpose technique for analysis of datasets with missing values. The approach is applicable to a variety of missing data patterns but often complicated by some restrictions like the type of variables to be imputed and the mechanism underlying the missing data. In this paper, the authors compare the performance of two multiple imputation methods, namely fully conditional specification and multivariate normal imputation in the presence of ordinal outcomes with monotone missing data patterns. Through a simulation study and an empirical example, the authors show that the two methods are indeed comparable meaning any of the two may be used when faced with scenarios, at least, as the ones presented here.     

Theory & Methods: Residual Diagnostic Plots for Checking for Model Mis‐specification in Time Series Regression

This paper considers residuals for time series regression. Despite much literature on visual diagnostics for uncorrelated data, there is little on the autocorrelated case. To examine various aspects of the fitted time series regression model, three residuals are considered. The fitted regression model can be checked using orthogonal residuals; the time series error model can be analysed using marginal residuals; and the white noise error component can be tested using conditional residuals. When used together, these residuals allow identification of outliers, model mis‐specification and mean shifts. Due to the sensitivity of conditional residuals to model mis‐specification, it is suggested that the orthogonal and marginal residuals be examined first.     

Common threshold in quantile regressions with an application to pricing for reputation

The paper develops a systematic estimation and inference procedure for quantile regression models where there may exist a common threshold effect across different quantile indices. We first propose a sup-Wald test for the existence of a threshold effect, and then study the asymptotic properties of the estimators in a threshold quantile regression model under the shrinking threshold effect framework. We consider several tests for the presence of a common threshold value across different quantile indices and obtain their limiting distributions. We apply our methodology to study the pricing strategy for reputation through the use of a data set from Taobao.com. In our economic model, an online seller maximizes the sum of the profit from current sales and the possible future gain from a targeted higher reputation level. We show that the model can predict a jump in optimal pricing behavior, which is considered as “reputation effect” in this paper. The use of threshold quantile regression model allows us to identify and explore the reputation effect and its heterogeneity in data. We find both reputation effects and common thresholds for a range of quantile indices in seller’s pricing strategy in our application.