Multilevel IRT models for the university teaching evaluation

In this paper, a generalization of the two-parameter partial credit model (2PL-PCM) and of two special cases, the partial credit model (PCM) and the rating scale model (RSM), with a hierarchical data structure will be presented. Having shown how 2PL-PCM, as with other item response theory (IRT) models, may be read in terms of a generalized linear mixed model (GLMM) with two aggregation levels, a presentation will be given of an extension to the case of measuring the latent trait of individuals aggregated in groups. The use of this Multilevel IRT model will be illustrated via reference to the evaluation of university teaching by students following the courses. The aim is to generate a ranking of teaching on the basis of student satisfaction, so as to give teachers, and those responsible for organizing study courses, a background of information that takes the opinions of the direct target group for university teaching (that is, the students) into account, in the context of improving the teaching courses available.     

基于G3指南的河北省煤矿企业社会责任评价体系的构建

评价一个煤矿企业履行社会责任好坏,需要有一套相对公平合理的评价体系.全球报告倡议组织发布的G3指南为建立煤矿企业社会责任体系提供了方向.本文对河北省煤矿企业社会责任现状进行了分析,指出基于G3指南的煤矿企业社会责任评价体系的构建还应考虑河北煤矿行业的特殊性,应建立员工、安全、经济、环境、社会五位一体的企业社会责任评价体系.     

Small-area estimation in the presence of area-level correlated responses

The Fay-Herriot area-level model for correlated response data is augmented with a between-groups-of-domains effect. Correlated-response parameters of small-area estimates no longer need the assumption of spatial contiguity. A simulation shows that area-level correlated-response observations increase the efficiency of the estimates, but do not reduce the biases.     

Fitting logistic multilevel models with crossed random effects via Bayesian Integrated Nested Laplace Approximations: a simulation study

Fitting cross-classified multilevel models with binary response is challenging. In this setting a promising method is Bayesian inference through Integrated Nested Laplace Approximations (INLA), which performs well in several latent variable models. We devise a systematic simulation study to assess the performance of INLA with cross-classified binary data under different scenarios defined by the magnitude of the variances of the random effects, the number of observations, the number of clusters, and the degree of cross-classification. In the simulations INLA is systematically compared with the popular method of Maximum Likelihood via Laplace Approximation. By an application to the classical salamander mating data, we compare INLA with the best performing methods. Given the computational speed and the generally good performance, INLA turns out to be a valuable method for fitting logistic cross-classified models.     

Dynamic approach to linear statistical calibration with an application in microwave radiometry

The problem of statistical calibration of a measuring instrument can be framed both in a statistical context as well as in an engineering context. In the first, the problem is dealt with by distinguishing between the ‘classical’ approach and the ‘inverse’ regression approach. Both of these models are static models and are used to estimate exact measurements from measurements that are affected by error. In the engineering context, the variables of interest are considered to be taken at the time at which you observe it. The Bayesian time series analysis method of Dynamic Linear Models can be used to monitor the evolution of the measures, thus introducing a dynamic approach to statistical calibration. The research presented employs a new approach to performing statistical calibration. A simulation study in the context of microwave radiometry is conducted that compares the dynamic model to traditional static frequentist and Bayesian approaches. The focus of the study is to understand how well the dynamic statistical calibration method performs under various signal-to-noise ratios, r.     

Rapid penalized likelihood-based outlier detection via heteroskedasticity test

Outlier detection is fundamental to statistical modelling. When there are multiple outliers, many traditional approaches in use are stepwise detection procedures, which can be computationally expensive and ignore stochastic error in the outlier detection process. Outlier detection can be performed by a heteroskedasticity test. In this article, a rapid outlier detection method via multiple heteroskedasticity test based on penalized likelihood approaches is proposed to handle these kinds of problems. The proposed method detects the heteroskedasticity of all data only by one step and estimate coefficients simultaneously. The proposed approach is distinguished from others in that a rapid modelling approach uses a weighted least squares formulation coupled with nonconvex sparsity-including penalization. Furthermore, the proposed approach does not need to construct test statistics and calculate their distributions. A new algorithm is proposed for optimizing penalized likelihood functions. Favourable theoretical properties of the proposed approach are obtained. Our simulation studies and real data analysis show that the newly proposed methods compare favourably with other traditional outlier detection techniques.     

On the maximization of likelihoods belonging to the exponential family using a Levenberg–Marquardt approach

The Levenberg–Marquardt algorithm is a flexible iterative procedure used to solve non-linear least-squares problems. In this work, we study how a class of possible adaptations of this procedure can be used to solve maximum-likelihood problems when the underlying distributions are in the exponential family. We formally demonstrate a local convergence property and discuss a possible implementation of the penalization involved in this class of algorithms. Applications to real and simulated compositional data show the stability and efficiency of this approach.     

Dependence between oil and commodities markets using time-varying Archimedean copulas and effectiveness of hedging strategies

The aim of this work is to study in a first step the dependence between oil and some commodity prices (cotton, rice, wheat, sucre, coffee, and silver) using copula theory, and then in a second step to determine the optimal hedging strategy for oil–commodity portfolio against the risk of negative variation in commodity markets prices. The model is implemented with an AR-GARCH model with innovations that follow t distribution for the marginal distribution and the extreme value copula for the joint distribution and parameters and dependence indices are re-estimated in each new day which allow taking into account nonlinear dependence, tails behavior, and their development over time. Various copula functions are used to model the dependence structure between oil and commodity markets. Empirical results show an increase in the dependence during the last 6 years. Volatility for commodity prices registered record levels in the same time with the increase in uncertainty. Optimal hedging ratio varies over time as a consequence of the change in the dependence structure.     

Multiple-index varying-coefficient models for longitudinal data

In haemodialysis patients, vascular access type is of paramount importance. Although recent studies have found that central venous catheter is often associated with poor outcomes and switching to arteriovenous fistula is beneficial, studies have not fully elucidated how the effect of switching of access on outcomes changes over time for patients on dialysis and whether the effect depends on switching time. In this paper, we characterise the switching access type effect on outcomes for haemodialysis patients. This is achieved by using a new class of multiple-index varying-coefficient (MIVC) models. We develop a new estimation procedure for MIVC models based on local linear, profile least-square method and Cholesky decomposition. Monte Carlo simulation studies show excellent finite sample performance. Finally, we analyse the dialysis data using our method.