试论汉字对汉语的适应与影响

文字与语言的关系十分密切 ,相互影响。汉字与汉语的关系也不例外 ,汉语的特点决定了汉字的存在形式和发展趋势 ,并使汉字形成之后具有许多自身独特的优势 ,这些优势反过来又对汉语的表现力产生一定的作用与影响。但即便如此 ,二者却有着本质的差别。认识和区别二者的不同本质 ,对理解汉字与汉语间的关系及正确地使用汉字和汉语都有着至关重要的作用。     

甲骨缀合十组

这里对甲骨资料的缀合共十组,其中前九组为作者缀合,最后一组已见于前人缀合成果,但未能引起注意,故作者在此特为指出。摹本之后是对相关卜辞含义的阐释。     

正确处理语文素养过程中的五大辩证关系

语文是口头和书面的语言和言语,语文学科是培养语文素质的基础人文学科,语文学科教育是学生在教师指导下的语文素养过程.语文素养过程应当遵循客观规律,正确处理好语言与言语、内容与形式、思维与语感、课堂与生活、教法与学法等各种辩证关系.     

Estimation and variable selection for generalised partially linear single-index models

In this paper, we study the problem of estimation and variable selection for generalised partially linear single-index models based on quasi-likelihood, extending existing studies on variable selection for partially linear single-index models to binary and count responses. To take into account the unit norm constraint of the index parameter, we use the ‘delete-one-component’ approach. The asymptotic normality of the estimates is demonstrated. Furthermore, the smoothly clipped absolute deviation penalty is added for variable selection of parameters both in the nonparametric part and the parametric part, and the oracle property of the variable selection procedure is shown. Finally, some simulation studies are carried out to illustrate the finite sample performance.     

Variable Selection for Panel Count Data via Non-Concave Penalized Estimating Function

Abstract.  Variable selection is an important issue in all regression analyses, and in this paper we discuss this in the context of regression analysis of panel count data. Panel count data often occur in long-term studies that concern occurrence rate of a recurrent event, and their analysis has recently attracted a great deal of attention. However, there does not seem to exist any established approach for variable selection with respect to panel count data. For the problem, we adopt the idea behind the non-concave penalized likelihood approach and develop a non-concave penalized estimating function approach. The proposed methodology selects variables and estimates regression coefficients simultaneously, and an algorithm is presented for this process. We show that the proposed procedure performs as well as the oracle procedure in that it yields the estimates as if the correct submodel were known. Simulation studies are conducted for assessing the performance of the proposed approach and suggest that it works well for practical situations. An illustrative example from a cancer study is provided.     

BAYESIAN HYPER‐LASSOS WITH NON‐CONVEX PENALIZATION

The Lasso has sparked interest in the use of penalization of the log‐likelihood for variable selection, as well as for shrinkage. We are particularly interested in the more‐variables‐than‐observations case of characteristic importance for modern data. The Bayesian interpretation of the Lasso as the maximum a posteriori estimate of the regression coefficients, which have been given independent, double exponential prior distributions, is adopted. Generalizing this prior provides a family of hyper‐Lasso penalty functions, which includes the quasi‐Cauchy distribution of Johnstone and Silverman as a special case. The properties of this approach, including the oracle property, are explored, and an EM algorithm for inference in regression problems is described. The posterior is multi‐modal, and we suggest a strategy of using a set of perfectly fitting random starting values to explore modes in different regions of the parameter space. Simulations show that our procedure provides significant improvements on a range of established procedures, and we provide an example from chemometrics.     

Selection of Latent Variables for Multiple Mixed‐outcome Models

Latent variable models have been widely used for modelling the dependence structure of multiple outcomes data. However, the formulation of a latent variable model is often unknown a priori, the misspecification will distort the dependence structure and lead to unreliable model inference. Moreover, multiple outcomes with varying types present enormous analytical challenges. In this paper, we present a class of general latent variable models that can accommodate mixed types of outcomes. We propose a novel selection approach that simultaneously selects latent variables and estimates parameters. We show that the proposed estimator is consistent, asymptotically normal and has the oracle property. The practical utility of the methods is confirmed via simulations as well as an application to the analysis of the World Values Survey, a global research project that explores peoples’ values and beliefs and the social and personal characteristics that might influence them.     

山西河津春节跳火民俗研究——以河津清涧镇为例

河津跳火民俗事项历史悠久,属拜火遗风。与我国其他地域的春节火民俗相比较,河津跳火的特点是:过程简洁、目的明确、材料单一、社区内化,并衍生出特殊的柏柴经济。跳火民俗的功能已由原始的驱鬼除疫逐渐变为当下的娱乐家众。     

Penalized regression models with autoregressive error terms

Penalized regression methods have recently gained enormous attention in statistics and the field of machine learning due to their ability of reducing the prediction error and identifying important variables at the same time. Numerous studies have been conducted for penalized regression, but most of them are limited to the case when the data are independently observed. In this paper, we study a variable selection problem in penalized regression models with autoregressive (AR) error terms. We consider three estimators, adaptive least absolute shrinkage and selection operator, bridge, and smoothly clipped absolute deviation, and propose a computational algorithm that enables us to select a relevant set of variables and also the order of AR error terms simultaneously. In addition, we provide their asymptotic properties such as consistency, selection consistency, and asymptotic normality. The performances of the three estimators are compared with one another using simulated and real examples.     

菲利浦·斯塔克设计理念探索

从研究法国设计大师菲利浦.斯塔克的设计风格和构思创意手段入手,剖析其设计的重要意义和影响,以中国设计师的视角去观察这个法国设计师如何突破现代主义思维方式,进而营造崭新的设计意境。