Entwicklung eines Programms zur kollegialen Introvisions-Supervision (KIS) für inner- und außerschulische Handlungsfelder

This article presents a new program of collegial supervision called ‘Collegial Introvision-Supervision’ (KIS). This phased program is based upon different concepts of collegial supervision (Pallasch, 1996; Spiess, 1996; Voss, 1996) and in particular on concepts of the TZI group supervision (Meister, 1996). The theoretical development is based on the ‘Integrative Introvision-Councelling’ and the practical foundation builds upon numerous supervision experiences of the author as well as upon tests in two projects of the professional education of teachers and social workers. A substantial aim of this new program is to find a procedure that optimises the perception of the factors taking part in the educational situation in order to make, based on that new perception, a new situational processing possible which allows a more appropriate educational reaction. The core of this change of perception is to reduce biographically caused aspects of a stress-generating estimation of the situation and to establish a calmer educational-acting mode.     

Estimation of the mean of the exponential distribution using moving extremes ranked set sampling

Moving Extremes Ranked Set Sampling (MERSS) is a useful modification of Ranked Set Sampling (RSS). Unlike RSS, MERSS allows for an increase of set size without introducing too much ranking error. The method is considered parametrically under exponential distribution. Maximum likelihood estimator (MLE), and a modified MLE are considered and their properties are studied. The method is studied under both perfect and imperfect ranking (with error in ranking). It appears that these estimators can be real competitors to the MLE using the usual simple random sampling (SRS).     

The finite-sample performance of robust unit root tests

This paper investigates the relative small sample performance of several robust unit root tests by means of a simulation study. It is confirmed that the traditional least-squares based Dickey-Fuller test has substantially lower power than several robust alternatives if the error distribution is fat-tailed while its power gain is small at the normal model. Particularly good results are achieved by a quasi-maximum likelihood test. However, all robust tests under consideration exhibit severe size distortions if the disturbances follow a skewed distribution. Moreover, under additive outliers, robust tests fail to produce stable sizes and good power properties. Consequently, the value of using robust unit root tests depends heavily of the type of nonnormality at hand.     

On influence diagnostic in univariate elliptical linear regression models

We discuss in this paper the assessment of local influence in univariate elliptical linear regression models. This class includes all symmetric continuous distributions, such as normal, Student-t, Pearson VII, exponential power and logistic, among others. We derive the appropriate matrices for assessing the local influence on the parameter estimates and on predictions by considering as influence measures the likelihood displacement and a distance based on the Pearson residual. Two examples with real data are given for illustration.     

When parties and candidates collide: citizen perception of house candidates' positions on abortion

When candidates assume issue positions opposite those of their sponsoring political party do citizens recognize these positions? Relatedly, what role do candidates' actual issue positions play in citizens' perceptions of their issue positions? Examining citizens' perceptions of 1996 and 1998 House candidates' position on abortion, this research finds that citizens' perceptions are shaped largely by partisan and, to a lesser extent, gender stereotypes. However, candidates' individuating positions on abortion influence perceptions of the candidates' position, but the effects are considerably stronger for perceptions of Republican candidates. Democratic candidates are likely to adopt anti-abortion positions in districts characterized by lower than average levels of political awareness and education, reducing the likelihood their party-contradicting position is accurately perceived. In contrast, Republican candidates adopt a pro-choice position in districts characterized by high education and political awareness, increasing the likelihood their position is accurately perceived.     

Obsessive-Compulsive Features in Pathological Lottery and Scratch-Ticket Gamblers

The results of this study support the notion that pathological gamblers drawn from the community would score higher on all three scores from the YBOCS than light gamblers. Consistent with hypotheses, pathological gamblers (lottery and scratch ticket) reported more obsessions, compulsions, and avoidance behavior than the light gamblers, and also reported having more urges to engage in injurious behaviors to themselves and others. These findings provide evidence that pathological gambling falls in a spectrum or family of disorders which have obsessive-compulsive disorder at its core. These findings support McElroy, Hudson, Philips, et al.'s (1993) suggestions of similarities between OCD and Impulse Control Disorders, and extend Blaszczynski (1999) findings of overlap between pathological gamblers and OCD in a treatment population. Heavy gamblers also reported significantly more hoarding symptoms and compulsive buying than light gamblers. More research in this area may show further evidence of a spectrum of disorders with obsessive compulsive disorder at its core, and show further links between impulse control disorders (such as pathological gambling) and OCD.     

日本民族学、文化人类学三题

战后是日本民族学发展的重要时期.本文简要概述了日本大学中民族学、文化人类学课程的缘起及其发展;"人类学"、"民族学"等诸词在日本各个时期的不同含义;日本女性文化人类学的研究状况.     

民间道教新房中术的产生

学术界普遍认为黄赤之道形成于东汉末年,始作俑者为五斗米道的创立者张陵.本文在分析时代背景和文献资料的基础上提出新观点,认为黄赤之道是两晋时期天师道的"新作".     

Kernel naive Bayes discrimination for high‐dimensional pattern recognition

Kernel discriminant analysis translates the original classification problem into feature space and solves the problem with dimension and sample size interchanged. In high‐dimension low sample size (HDLSS) settings, this reduces the ‘dimension’ to that of the sample size. For HDLSS two‐class problems we modify Mika's kernel Fisher discriminant function which – in general – remains ill‐posed even in a kernel setting; see Mika et al. (1999). We propose a kernel naive Bayes discriminant function and its smoothed version, using first‐ and second‐degree polynomial kernels. For fixed sample size and increasing dimension, we present asymptotic expressions for the kernel discriminant functions, discriminant directions and for the error probability of our kernel discriminant functions. The theoretical calculations are complemented by simulations which show the convergence of the estimators to the population quantities as the dimension grows. We illustrate the performance of the new discriminant rules, which are easy to implement, on real HDLSS data. For such data, our results clearly demonstrate the superior performance of the new discriminant rules, and especially their smoothed versions, over Mika's kernel Fisher version, and typically also over the commonly used naive Bayes discriminant rule.