An approach to measure distance between compositional diet estimates containing essential zeros

For many applications involving compositional data, it is necessary to establish a valid measure of distance, yet when essential zeros are present traditional distance measures are problematic. In quantitative fatty acid signature analysis (QFASA), compositional diet estimates are produced that often contain many zeros. In order to test for a difference in diet between two populations of predators using the QFASA diet estimates, a legitimate measure of distance for use in the test statistic is necessary. Since ecologists using QFASA must first select the potential species of prey in the predator's diet, the chosen measure of distance should be such that the distance between samples does not decrease as the number of species considered increases, a property known in general as subcompositional coherence. In this paper we compare three measures of distance for compositional data capable of handling zeros, but not satisfying some of the well-accepted principles of compositional data analysis. For compositional diet estimates, the most relevant of these is the property of subcompositionally coherence and we show that this property may be approximately satisfied. Based on the results of a simulation study and an application to real-life QFASA diet estimates of grey seals, we recommend the chi-square measure of distance.     

Computation of an efficient and robust estimator in a semiparametric mixture model

In this article, we propose an efficient and robust estimation for the semiparametric mixture model that is a mixture of unknown location-shifted symmetric distributions. Our estimation is derived by minimizing the profile Hellinger distance (MPHD) between the model and a nonparametric density estimate. We propose a simple and efficient algorithm to find the proposed MPHD estimation. Monte Carlo simulation study is conducted to examine the finite sample performance of the proposed procedure and to compare it with other existing methods. Based on our empirical studies, the newly proposed procedure works very competitively compared to the existing methods for normal component cases and much better for non-normal component cases. More importantly, the proposed procedure is robust when the data are contaminated with outlying observations. A real data application is also provided to illustrate the proposed estimation procedure.     

Diagnostics for quasi-likelihood nonlinear models

Quasi-likelihood nonlinear models (QLNMs) are an extension of generalized linear model and include a widen class of models as special cases. This article investigates some diagnostic methods in QLNMs. An equivalency between a case-deletion model and a mean-shift outlier model in QLNM is established. Two simulation study and a real dataset are used to illustrate the proposed diagnostic methods.     

Bayesian estimation and influence diagnostics of generalized partially linear mixed-effects models for longitudinal data

This paper develops a Bayesian approach to obtain the joint estimates of unknown parameters, nonparametric functions and random effects in generalized partially linear mixed models (GPLMMs), and presents three case deletion influence measures to identify influential observations based on the φ-divergence, Cook's posterior mean distance and Cook's posterior mode distance of parameters. Fisher's iterative scoring algorithm is developed to evaluate the posterior modes of parameters in GPLMMs. The first-order approximation to Cook's posterior mode distance is presented. The computationally feasible formulae for the φ-divergence diagnostic and Cook's posterior mean distance are given. Several simulation studies and an example are presented to illustrate our proposed methodologies.     

Detection of outliers in simple circular regression models using the mean circular error statistic

The investigation on the identification of outliers in linear regression models can be extended to those for circular regression case. In this paper, we propose a new numerical statistic called mean circular error to identify possible outliers in circular regression models by using a row deletion approach. Through intensive simulation studies, the cut-off points of the statistic are obtained and its power of performance investigated. It is found that the performance improves as the concentration parameter of circular residuals becomes larger or the sample size becomes smaller. As an illustration, the statistic is applied to a wind direction data set.     

基于有效预测区域的模糊数据关联

多目标多传感器跟踪系统由数据关联和目标状态估计两部分组成,数据关联是多目标跟踪系统研究的核心。数据关联和目标状态估计两部分既有一定的独立性又有密切的联系,而将两部分合理地结合对提高跟踪系统的性能是重要的。该文以跟踪目标的有效预测区域为依据,利用基于Mahalanobis距离的模糊均值聚类方法解决数据关联问题,在一定程度上将数据关联和目标状态估计两个不同的过程相结合,仿真计算说明了其有效性。     

Psychic distance, psychic distance paradox and behavioral economics: Modeling MNC entry behavior in foreign markets

Assuming that MNCs face a much more complex environment that local enterprizes, the paper begins by discussing what economist Beckerman called psychic distance. After a historical discussion of this concept, I also discuss what O’Gardy and Lane called psychic distance paradox. Then, I argue that these two concepts have a great deal of relevance beyond their original intent of international trade-they are also relevant to FDI and all other formas of international production and exchange. Next, I argue, as I have done in several previous papers, that behavioral economics has a great deal of relevance to FDI and international productive activity; I also argue that behavioral economics can be utilized to describe the concepts of psychic distance and psychic distance paradox. Then, I develop a behavioral economics-based model that can explain the concepts of psychic distance and psychic distance paradox, and their relevance to the modes of entry of MNCs in international markets. In doing so, I argue that FDI and MNC behavior in general need not be explained outside of economics, since, in contrast to neo-classical economics, behavioral economics is capable of capturing the complexities of global markets.     

网络远程学习中的低绩效现象及解决方案

在网络远程学习中，由于师生时空分离，学生面对大量的信息会出现无所适从、信息迷航现象，从而造成学习的低绩效。这既有来自学习者自身的原因，也有来自于学习资源和学习支持方面的原因，教育部门应通过培养学生主动学习习惯、改善教学设计、营造良好的人文环境三方面，来有效解决这一问题。     

Ranking opportunity sets in the space of functionings

We develop a ranking of compact, convex and comprehensive opportunity sets defined in the evaluative space of individual functionings. We suppose the existence of a target, that is a multi-dimensional bliss point in terms of functionings. This leads us to define concepts such as essentiality and freedomin a novel way. As a main result, we give an axiomatic characterization of the ranking obtained by minimizing the Euclidean distance between each opportunity set and the target.     

远程学习的基本特征

远程学习者与传统学习者相比,就素质而言具有差异性,就环境而言具有个体性,就模式而言具有自主性,就学习规律而言具有通过媒体的互动性。远程学习的这些特性要求学习者内部具有知识准备、积极的学习动机和正确的学习策略,外部拥有高效率的媒体设施和丰富的学习资源。