离子晶体外表面束缚极化子

本文计及表面光学声子的作用以及像电荷的作用,采用变分方法对一些材料外表面束缚极化子的基态能量和自陷能作了数值计算,并对结果进行了讨论,发现:对于Ⅰ—Ⅶ、Ⅱ—Ⅵ,Ⅲ—Ⅴ化合物的外表面,均可形成稳定的束缚极化子,且由于表面光学声子的作用,使这一杂质态更加稳定。     

How well is debt managed by nonprofits?

Little is known about why nonprofits accrue debt, how much they owe, and whether the funds they borrow are used productively. This article distinguishes between productive, problematic, and deferred debt. Employing a data base representative of 114,726 tax-filing charitable nonprofits in the United States in 1986, it examines the pervasiveness of nonprofit debt and the relation between this debt and nonprofit financial health. The analysis finds that over 70 percent of the nonprofits hold debt, the distribution of this debt is highly concentrated, and the level of debt and leverage varies with asset size and type of activity. Nonprofits with higher leverage and absolute debt levels are financially healthier than those with lower levels. While the analysis does not determine whether financially stronger nonprofits are better able to borrow, the results support the view that borrowing in the nonprofit sector is economically efficient.     

当代印度政坛中的雅利安人入侵理论争议初探

长期以来,由于受西方知识界对印欧文化百年研究积累的影响,＂雅利安人入侵理论＂始终位居学界主流,并进入印度与世界各国的教科书中,成为我们诠释印度古代历史的重要基础;时至今日,在后殖民学术批判风潮与印度教民族主义的推波助澜之下,此一理论却俨然成为具有高度政治争议性的论题。影响所及,不光是在印度国内的学界,更波及全世界的印度学研究社群。究竟对于＂雅利安人入侵理论＂的争论如何影响当代印度政治发展？此一理论为什么又能引起如此惊人的政治效应？各方对于此一理论的基本立场差异又是如何？透过本文的讨论,我们希望能针对这些问题提出回答。     

Estimating population sizes by catch-effort methods

Estimating population sizes by the catch-effort methods is of enormous importance, in particular to harvest animal populations. A unified mixture model is introduced for different catchability functions to account for heterogeneous catchabilities among individual animals. A sequence of lower bounds to the odds that a single animal is not caught are proposed and used to define pseudo maximum likelihood estimators for the population size. The one-sided nature of confidence intervals is discussed. The proposed estimation methods are presented and illustrated by numerical studies.     

Multiple-arm superiority and non-inferiority designs with various endpoints

Multiple-arm dose-response superiority trials are widely studied for continuous and binary endpoints, while non-inferiority designs have been studied recently in two-arm trials. In this paper, a unified asymptotic formulation of a sample size calculation for k-arm (k>0) trials with different endpoints (continuous, binary and survival endpoints) is derived for both superiority and non-inferiority designs. The proposed method covers the sample size calculation for single-arm and k-arm (k> or =2) designs with survival endpoints, which has not been covered in the statistic literature. A simple, closed form for power and sample size calculations is derived from a contrast test. Application examples are provided. The effect of the contrasts on the power is discussed, and a SAS program for sample size calculation is provided and ready to use.     

A Likelihood-Based Approximation to the Cumulative Distribution Function of the Noncentral t Distribution

Optimal designs for estimating the parameters and also the optimum factor combinations in multiresponse experiments have been considered by various authors. However, till date, in mixture experiments optimum designs have been studied only in the single response case. In this article, attempt has been made to investigate optimum designs for estimating optimum mixing proportions in a multiresponse mixture experiment.     

Multi-Step Classification Trees

Many algorithms originated from decision trees have been developed for classification problems. Although they are regarded as good algorithms, most of them suffer from loss of prediction accuracy, namely high misclassification rates when there are many irrelevant variables. We propose multi-step classification trees with adaptive variable selection (the multi-step GUIDE classification tree (MG) and the multi-step CRUISE classification tree (MC) to handle this problem. The variable selection step and the fitting step comprise the multi-step method.

We compare the performance of classification trees in the presence of irrelevant variables. MG and MC perform better than Random Forest and C4.5 with an extremely noisy dataset. Furthermore, the prediction accuracy of our proposed algorithm is relatively stable even when the number of irrelevant variables increases, while that of other algorithms worsens.     

Variable Selection via Regression Trees in the Presence of Irrelevant Variables

Many tree algorithms have been developed for regression problems. Although they are regarded as good algorithms, most of them suffer from loss of prediction accuracy when there are many irrelevant variables and the number of predictors exceeds the number of observations. We propose the multistep regression tree with adaptive variable selection to handle this problem. The variable selection step and the fitting step comprise the multistep method.

The multistep generalized unbiased interaction detection and estimation (GUIDE) with adaptive forward selection (fg) algorithm, as a variable selection tool, performs better than some of the well-known variable selection algorithms such as efficacy adaptive regression tube hunting (EARTH), FSR (false selection rate), LSCV (least squares cross-validation), and LASSO (least absolute shrinkage and selection operator) for the regression problem. The results based on simulation study show that fg outperforms other algorithms in terms of selection result and computation time. It generally selects the important variables correctly with relatively few irrelevant variables, which gives good prediction accuracy with less computation time.     

Frequency of Selecting Noise Variables in Subset Regression Analysis: A Simulation Study

This article presents the results of a simulation study of variable selection in a multiple regression context that evaluates the frequency of selecting noise variables and the bias of the adjusted R ² of the selected variables when some of the candidate variables are authentic. It is demonstrated that for most samples a large percentage of the selected variables is noise, particularly when the number of candidate variables is large relative to the number of observations. The adjusted R ² of the selected variables is highly inflated.