The estimation of multidimensional fixed effects panel data models

This article introduces the appropriate within estimators for the most frequently used three-dimensional fixed effects panel data models. It analyzes the behavior of these estimators in the cases of no self-flow data, unbalanced data, and dynamic autoregressive models. The main results are then generalized for higher dimensional panel data sets as well.     

Distance-Based Measures of Association with Applications in Relating Hyperspectral Images

We propose a distance-based method to relate two data sets. We define and study some measures of multivariate association based on distances between observations. The proposed approach can be used to deal with general data sets (e.g., observations on continuous, categorical or mixed variables). An application, using Hellinger distance, provides the relationships between two regions of hyperspectral images.     

A convex quadratic semi-definite programming approach to the partial additive constant problem in multidimensional scaling

Bénasséni [Partial additive constant, J. Statist. Comput. Simul. 49 (1994), pp. 179–193] studied the partial additive constant problem in multidimensional scaling. This problem is quite challenging to solve, and Bénasséni proposed a numerical procedure for two special cases: the cross-set partial perturbation and the within-set partial perturbation. This paper casts the problem as a modern quadratic semi-definite programming (QSDP) problem, which is not only capable of dealing with general cases, but also enjoys a number of good properties. One of the good properties is that the proposed approach can find the minimal constant under very weak conditions. Another is that there exists a ready-to-use numerical package such as the QSDP solver in Toh [An inexact path-following algorithm for convex quadratic SDP, Math. Program. 112 (2008), pp. 221–254], allowing a great deal of flexibility in choosing the index set to which the partial constant should be added. Our numerical results show a significant improvement over that reported in Bénasséni (1994).     

Estimation of multivariate conditional-tail-expectation using Kendall's process

This paper deals with the problem of estimating the multivariate version of the Conditional-Tail-Expectation, proposed by Di Bernardino et al. [(2013), ‘Plug-in Estimation of Level Sets in a Non-Compact Setting with Applications in Multivariable Risk Theory’, ESAIM: Probability and Statistics, (17), 236–256]. We propose a new nonparametric estimator for this multivariate risk-measure, which is essentially based on Kendall's process [Genest and Rivest, (1993), ‘Statistical Inference Procedures for Bivariate Archimedean Copulas’, Journal of American Statistical Association, 88(423), 1034–1043]. Using the central limit theorem for Kendall's process, proved by Barbe et al. [(1996), ‘On Kendall's Process’, Journal of Multivariate Analysis, 58(2), 197–229], we provide a functional central limit theorem for our estimator. We illustrate the practical properties of our nonparametric estimator on simulations and on two real test cases. We also propose a comparison study with the level sets-based estimator introduced in Di Bernardino et al. [(2013), ‘Plug-In Estimation of Level Sets in A Non-Compact Setting with Applications in Multivariable Risk Theory’, ESAIM: Probability and Statistics, (17), 236–256] and with (semi-)parametric approaches.     

Building up adjusted indicators of students’ evaluation of university courses using generalized item response models

This article advances a proposal for building up adjusted composite indicators of the quality of university courses from students’ assessments. The flexible framework of Generalized Item Response Models is adopted here for controlling the sources of heterogeneity in the data structure that make evaluations across courses not directly comparable. Specifically, it allows us to: jointly model students’ ratings to the set of items which define the quality of university courses; explicitly consider the dimensionality of the items composing the evaluation form; evaluate and remove the effect of potential confounding factors which may affect students’ evaluation; model the intra-cluster variability at course level. The approach simultaneously deals with: (i) multilevel data structure; (ii) multidimensional latent trait; (iii) personal explanatory latent regression models. The paper pays attention to the potential of such a flexible approach in the analysis of students evaluation of university courses in order to explore both how the quality of the different aspects (teaching, management, etc.) is perceived by students and how to make meaningful comparisons across them on the basis of adjusted indicators.     

Some adjusted orthogonal,variance balanced,multidimensional block designs

A multidimensional block design (MBD) is an experimental design with d > 1 blocking criteria geometrically represented as a d-dimensional lattice with treatment varieties assigned to some or all nodes of the lattice. Intrablock analysis of variance tables for some special classes of two- and three-dimensional block designs with some empty nodes are given. Design plans and efficiencies for 31 two-dimensional designs, each universally optimal in defined classes of designs, and 7 three-dimensional designs, each nearly optimal in defined classes of designs, are listed in the appendices. A need for such designs is apparent when the blocking criteria are implemented successively and empty nodes do not represent wasted experimental units.     

西部地方财经类院校双语教学课程多维创新机制

随着全球经济一体化持续发展,对兼顾专业知识与跨文化交流能力的双语复合型人才的需求不断增加,推动了双语教学课程创新的步伐。本文通过剖析双语教学课程融合创新内涵,明确双语教学课程融合创新的目标和导向,并从课程结构、教学内容、教学策略和教学模式等多个维度,构建西部财经类院校双语教学课程融合创新机制,进一步推进双语课程改革创新和双语创新人才培养工作。     

国内外老年人群多维健康评估研究进展

随着人口老龄化进程的加快,老年人群的健康状况越来越受到重视。老年人的健康状况是变化的、动态的,因此,对于老年健康也应该全面、多维度、准确地评价。不同的多维健康评估结果,其对应的资源分配、慢性病防治、养老模式等也有所不同。文章通过梳理国内外多维健康评估进展,以期为开发和完善老年多维健康评估并为促进我国健康老龄化发展提供参考。     

Poverty assessment as a multidimensional socio-economic concept: the case of the Rawalpindi region in Pakistan

The definition on poverty has progressed into a multidimensional concept focusing more on socio-economic dimensions than being a mere measure of financial deprivation. Corresponding to this advancement, this study investigated the incidence of multidimensional poverty (MDP) in the Rawalpindi region of Pakistan, taking into account three dimensions, i.e. education, health and housing. Findings revealed an inconsistent declining trend of MDP over time. This inconsistency was mainly attributed to observed fluctuations in deprivation levels of education, health and housing in the region. Over time, education deprivation showed a significant net increase. Notably, the magnitude of MDP in rural areas was higher than in urban areas.