实践性知识:教师专业生活场景中的新视角

笔者认为教师的实践性知识应称为"互动场景中的经验总和".那么教师专业知识来源于何处?教师是如何在专业场景中提升实践性知识的?笔者在教师实践性知识的研究中,试图从教师专业生活中梳理出几块实践性知识产生的场景,在较为具体场景中展开分析.笔者试图描绘的场景包括三个方面:课堂教学、师生互动和师师互动、个人生活.     

我国区域经济不平衡增长的现状、成因与效应分析

本文从我国区域经济发展不平衡的现状出发,以我国的区域经济理论为依据,分析了我国区域经济发展增长不平衡的原因,以及由不平衡增长所产生的聚集效应、结构效应、辐射效应和示范效应。     

我国区域金融发展与经济增长的关系研究——基于面板数据的实证研究

运用1980—2004年间相应经济指标的面板数据对我国东、中、西部经济增长与金融发展进行了实证分析,结果表明,中西部地区更多的依靠信贷规模扩张来推动经济增长,而东部地区更多的依赖于金融效率的提升。     

居民收入分配差距扩大对经济增长的效用分析

收入分配、消费需求与经济增长之间是相互联系、相互促进的辩证统一关系。但是我国近年来的发展却出现了经济增长快,居民收入增长低于经济增长的幅度,从而导致消费需求降低,消费对GDP增长的贡献份额连续大幅度下降的态势。在对居民收入分配差距、城乡差距、地区差距实证分析的基础上,从理论上分析了收入分配差距对经济增长的巨大影响,得出我国收入分配差距以及城乡差距和地区差距已经极大地制约了人们的消费需求,进而影响到经济的持续、有序、快速发展的结论。中央提出:"扩大内需是我国经济发展的长期战略方针和基本立足点。"扩大内需的前提是调整收入分配政策,大幅度提高居民的收入水平,重点增加低收入者的收入,促进整个社会消费水平的提高,从而推动国民经济的持续增长。     

“增长型衰退”与出口商品结构——关于外贸出口问题的若干思考

本文分析了中国近 5年来外贸出口增长速度及外贸出口效益的变化趋势 ,并用“增长型衰退”对其加以表述 ;一个国家的外贸出口难于做到线型增长 ,但中国的外贸出口增长速度下滑 ,却跟外贸出口的外延增长方式以及出口商品结构变化缓慢有关。通过创新以提升出口商品结构 ,是我国外贸出口走可持续发展的必由之路     

经济制度与经济增长

人们通常认为 ,经济增长是由资本、技术、劳动力等生产要素的投入带来的 ,不考虑或很少考虑经济增长的制度因素 ,而只是把它作为研究经济增长的已知的、既定的前提或外生的变量。然而 ,制度创新却能在物质生产要素不变尤其是技术不变时提高生产率 ,促进经济增长。制度因素对经济增长起着客观的制约作用。因此 ,研究经济增长不能撇开制度因素     

On the power of goodness-of-fit tests for the exponential distribution under progressive Type-II censoring

The aim of this article is to review existing goodness-of-fit tests for the exponential distribution under progressive Type-II censoring and to provide some new ideas and adjustments. In particular, we consider two-parameter exponentially distributed random variables and adapt the proposed test procedures to our scenario if necessary. Then, we compare their power by an extensive simulation study. Furthermore, we propose five new test procedures that provide reasonable alternatives to those already known.     

Progressive censoring with fixed censoring times

In this paper, we revisit the progressive Type-I censoring scheme as it has originally been introduced by Cohen [Progressively censored samples in life testing. Technometrics. 1963;5(3):327–339]. In fact, original progressive Type-I censoring proceeds as progressive Type-II censoring but with fixed censoring times instead of failure time based censoring times. Apparently, a time truncation has been added to this censoring scheme by interpreting the final censoring time as a termination time. Therefore, not much work has been done on Cohens's original progressive censoring scheme with fixed censoring times. Thus, we discuss distributional results for this scheme and establish exact distributional results in likelihood inference for exponentially distributed lifetimes. In particular, we obtain the exact distribution of the maximum likelihood estimator (MLE). Further, the stochastic monotonicity of the MLE is verified in order to construct exact confidence intervals for both the scale parameter and the reliability.     

A new estimator of Kullback–Leibler information and its application in goodness of fit tests*

We propose here a general statistic for the goodness of fit test of statistical distributions. The proposed statistic is constructed based on an estimate of Kullback–Leibler information. The proposed test is consistent and the limiting distribution of the test statistic is derived. Then, the established results are used to introduce goodness of fit tests for the normal, exponential, Laplace and Weibull distributions. A simulation study is carried out for examining the power of the proposed test and to compare it with those of some existing procedures. Finally, some illustrative examples are presented and analysed, and concluding comments are made.     

Quantile regression for large-scale data via sparse exponential transform method

In recent decades, quantile regression has received much more attention from academics and practitioners. However, most of existing computational algorithms are only effective for small or moderate size problems. They cannot solve quantile regression with large-scale data reliably and efficiently. To this end, we propose a new algorithm to implement quantile regression on large-scale data using the sparse exponential transform (SET) method. This algorithm mainly constructs a well-conditioned basis and a sampling matrix to reduce the number of observations. It then solves a quantile regression problem on this reduced matrix and obtains an approximate solution. Through simulation studies and empirical analysis of a 5% sample of the US 2000 Census data, we demonstrate efficiency of the SET-based algorithm. Numerical results indicate that our new algorithm is effective in terms of computation time and performs well for large-scale quantile regression.