A reflected feature space for CART

We present an algorithm for learning oblique decision trees, called HHCART(G). Our decision tree combines learning concepts from two classification trees, HHCART and Geometric Decision Tree (GDT). HHCART(G) is a simplified HHCART algorithm that uses linear structure in the training examples, captured by a modified GDT angle bisector, to define splitting directions. At each node, we reflect the training examples with respect to the modified angle bisector to align this linear structure with the coordinate axes. Searching axis parallel splits in this reflected feature space provides an efficient and effective way of finding oblique splits in the original feature space. Our method is much simpler than HHCART because it only considers one reflected feature space for node splitting. HHCART considers multiple reflected feature spaces for node splitting making it more computationally intensive to build. Experimental results show that HHCART(G) is an effective classifier, producing compact trees with similar or better results than several other decision trees, including GDT and HHCART trees.     

Classification with minimum ambiguity under distribution heterogeneity

The traditional classification is based on the assumption that distribution of indicator variable X in one class is homogeneous. However, when data in one class comes from heterogeneous distribution, the likelihood ratio of two classes is not unique. In this paper, we construct the classification via an ambiguity criterion for the case of distribution heterogeneity of X in a single class. The separated historical data in each situation are used to estimate the thresholds respectively. The final boundary is chosen as the maximum and minimum thresholds from all situations. Our approach obtains the minimum ambiguity with a high classification accuracy allowing for a precise decision. In addition, nonparametric estimation of the classification region and theoretical properties are derived. Simulation study and real data analysis are reported to demonstrate the effectiveness of our method.     

A seasonal mixed-POT model to estimate high flood quantiles from different event types and seasons

Flood events can be caused by several different meteorological circumstances. For example, heavy rain events often lead to short flood events with high peaks, whereas snowmelt normally results in events of very long duration with a high volume. Both event types have to be considered in the design of flood protection systems. Unfortunately, all these different event types are often included in annual maximum series (AMS) leading to inhomogeneous samples. Moreover, certain event types are underrepresented in the AMS. This is especially unsatisfactory if the most extreme events result from such an event type. Therefore, monthly maximum data are used to enlarge the information spectrum on the different event types. Of course, not all events can be included in the flood statistics because not every monthly maximum can be declared as a flood. To take this into account, a mixture Peak-over-threshold model is applied, with thresholds specifying flood events of several types that occur in a season of the year. This model is then extended to cover the seasonal type of the data. The applicability is shown in a German case study, where the impact of the single event types in different parts of a year is evaluated.     

Miaoicization vis-à-vis sinicization: contrasting Miao subgroups in contemporary China

This article studies the Miao classification as an example to examine the minzu shibie project initiated by the Chinese Communist Party after 1949. The Miao classification that originates with the minzu shibie project can be defined as a process of miaoicization; however, it is not a unified Miao group (as projected by the classification team) that has emerged but rather one dominant Miao subgroup that defines the official Miao category in contemporary China. At the moment the most dominant Miao subgroup is the Hmu group in Southeast Guizhou. The overemphasis of Hmu sub-culture to represent all the Miao of China is related to the fact that over the last decades many important leaders in Guizhou have been Hmu. While all Hmu are sinicized to a certain extent, it seems that the Hmu at the bottom of the socioeconomic scale are most likely to be sinicized and to amalgamate with the Han majority.     

康定市吉居藏语所属问题

本文针对分布在四川甘孜州康定市吉居乡的藏族语言进行分析。吉居话的语言所属在以往研究里没有共识，有属于扎巴语的意见和属于康巴藏语的意见。通过与藏文的对比，可见该语言为一个属于康巴藏语崩波岗方言群的土话，并非木雅语或扎巴语。基于与吉居话周边土话的比较，我们提出吉居话属于崩波岗方言群雅砻江组的假设性结论。本文的分析方法亦能作为识别康巴藏语中方言所属的参考。     

A panel study of migration,self-selection and household real income

The impact of migration on income for Swedish multi-adult households is examined using panel data pertaining to a sample of stable household constellations during the period 1980–1990. In contrast to previous studies, data on household disposable income is employed in estimating the income function. The empirical results indicate no significant effect on real disposable income from migration. In addition, the hypothesis of no self-selection, or zero correlation between the errors in the decision function and the income function, cannot be rejected. Received: 10 May 1995 / Accepted: 14 August 1996     

浅谈藏文古文献的开发利用

本文论述了藏文古文献常见金石铭刻、木简、文书写卷、木刻版文献这四种类型以及各种类型的渊源,在不同历史条件下所起的作用。抢救和保护藏文古文献是目前当务之急。在藏文古文献的整理中,应重视文献的永在价值和潜在价值。藏文文献的开发利用应从宣传介绍、吸引更多的人重视和利用藏文古文献、改进服务方式、建立藏文古文献开发利用的领导机构、培养古文献整理人才几方面着手,使藏文古文献在藏学研究中发挥出更大的作用     

民族旅游地居民分类与支持行为： 基于文化适应理论的视角

在民族旅游地的发展中,居民态度及主客之间的关系是旅游产业需要解决的关键问题.基于文化适应理论,通过开发Berry文化适应二维模型,对贵州黔东南黎平肇兴侗寨241位社区居民样本的实证研究显示:旅游情境中,居民在主客跨文化互动中具有整合、同化、分离、边缘四种文化适应类型,并以整合型居多;整合与同化意味着文化适应程度高,分离和边缘意味着文化适应程度低;女性的文化适应程度比男性高;文化适应程度越高的居民对旅游产业的支持度越高,具体而言,文化适应能力较强的居民对旅游者更加热情,也更愿意为目的地进行推荐宣传. 因此,旅游规划和管理部门应重视并着力提升民族旅游地居民在旅游发展中的文化适应能力,使其既能自信地展示和传承民俗文化,又能与旅游者进行热情、友好的互动,从而在文化、经济等层面推动目的地的可持续发展.