Sparse Bayesian variable selection in multinomial probit regression model with application to high-dimensional data classification

Here we consider a multinomial probit regression model where the number of variables substantially exceeds the sample size and only a subset of the available variables is associated with the response. Thus selecting a small number of relevant variables for classification has received a great deal of attention. Generally when the number of variables is substantial, sparsity-enforcing priors for the regression coefficients are called for on grounds of predictive generalization and computational ease. In this paper, we propose a sparse Bayesian variable selection method in multinomial probit regression model for multi-class classification. The performance of our proposed method is demonstrated with one simulated data and three well-known gene expression profiling data: breast cancer data, leukemia data, and small round blue-cell tumors. The results show that compared with other methods, our method is able to select the relevant variables and can obtain competitive classification accuracy with a small subset of relevant genes.     

工作搜索行为研究的回顾和展望

工作搜索行为是个体付出努力和时间,获取潜在的工作信息的有目的的意志性行为模式.关于工作搜索行为的结构有成分说、阶段说和维度说.影响工作搜索行为的因素主要有个体的自尊、自我效能感、工作搜索动机以及人格特质等,而个体的工作搜索行为又会影响到个体的就业情况和就业质量.未来的研究可以从以下方面做出努力:对不同的亚群体分别进行具体研究;使多种研究方法相互交叉来补充以往研究的不足;进一步拓展研究领域、发展工作搜索行为的理论以及建构完整的模型;在应用领域中进一步研究工作搜索行为的干预与培训措施的有效性.     

一种求解双目标flow shop排序问题的进化算法

提出一种求解双目标flow shop排序的递进多目标进化算法.算法采用改进的精英复制策略,在实现精英保留的前提下降低了计算复杂性;通过递进进化模式增加群体多样性,改善了算法收敛性;通过群体进化过程中对非劣解集进行竞争型可变邻域启发式搜索,增强了算法局部搜索性能.采用新算法和参照算法NSGA-II对31个标准双目标flow shop算例进行优化.研究结果表明,新算法在所有算例的求解中均获得了优于NSGA-II的非劣解集,验证了算法的有效性.     

现代信息查询领域知识体系探讨

在现代网络环境下,传统的文献检索范围已迅速拓展到现代信息查询领域,作为大学信息教育的课程——《文献检索与利用》,其知识体系面临淘汰陈旧知识、更新内容、体系结构优化重组的课题。本文针对这一目标,对现代信息查询领域知识体系作了初步的探讨。     

Causal inferences on the effectiveness of complex social programs: Navigating assumptions,sources of complexity and evaluation design challenges

This paper explores avenues for navigating evaluation design challenges posed by complex social programs (CSPs) and their environments when conducting studies that call for generalizable, causal inferences on the intervention’s effectiveness. A definition is provided of a CSP drawing on examples from different fields, and an evaluation case is analyzed in depth to derive seven (7) major sources of complexity that typify CSPs, threatening assumptions of textbook-recommended experimental designs for performing impact evaluations. Theoretically-supported, alternative methodological strategies are discussed to navigate assumptions and counter the design challenges posed by the complex configurations and ecology of CSPs. Specific recommendations include: sequential refinement of the evaluation design through systems thinking, systems-informed logic modeling; and use of extended term, mixed methods (ETMM) approaches with exploratory and confirmatory phases of the evaluation. In the proposed approach, logic models are refined through direct induction and interactions with stakeholders. To better guide assumption evaluation, question-framing, and selection of appropriate methodological strategies, a multiphase evaluation design is recommended.     

Step-wise group screening with equal prior probabilities and no errors in observations

This paper introduces a screening procedure called step-wise group screening for isolating defective factors from a population consisting of defective (or important) and non-defective     

Performance of traditional f tests in repeated measures designs under covariance heterogeneity

This study investigates the performance of two traditional F tests, one for main effects and the other for interaction in repeated measures designs under several conditions of covariance heterogeneity. Overall, the test for interaction is more vulnerable than the one for main effects. Distortion in the level of significance is less serious for the case of equal group size.     

Confounding in mixed factorial experiments through isomorphisms

The derivation of a simpie mexhoa for confounding in mixed factorial experiments from an isomorphism of finite abelian groups is presented. The theoretical bases of confounding procedures that use modular arithmetic for such experiments are compared.     

Optimal design of complex experiments with qualitative factors of influence

A complex experiment with qualirarive factors influencing the outcome of the experiment can be seen as a general ANOVA setup. A design of such an experiment will be the assignment at which of the possible levels of the factors the actual experiment should be performed. In this paper optimal designs of such experiments will be characterized with respect to three different optimality criteria including the so called uniform optimality of a design. The possible applications of the main optimization result providing these characterizations can be used to more general experiments. The particular results on these generalizations will be indicated at the end of this paper.     

A Class of Sampling Two Units with Probability Proportional to Size

A class of sampling two units without replacement with inclusion probability proportional to size is proposed in this article. Many different well known probability proportional to size sampling designs are special cases from this class. The first and second inclusion probabilities of this class satisfy important properties and provide a non-negative variance estimator of the Horvitz and Thompson estimator for the population total. Suitable choice for the first and second inclusion probabilities from this class can be used to reduce the variance estimator of the Horvitz and Thompson estimator. Comparisons between different proportional to size sampling designs through real data and artificial examples are given. Examples show that the minimum variance of the Horvitz and Thompson estimator obtained from the proposed design is not attainable for the most cases at any of the well known designs.