特征AHP在高校教师教学质量评定中的应用

本文针对高校教师教学质量的评定提出了一种特征层次分析法。首先通过分析教学质量的影响因素,建立三层层次分析模型。在其基础上引入特征法,选取特征群并确定特征群中个体的权重,利用反算法根据组合权重及方案层权重,求取准则层评定指标权重,避免了不同质指标间对比的不确定性,摒除了专家直接给出评定指标对比矩阵的主观性,减少了对教师评定过程的不一致性和不完备性。     

浙江省新生代农民工继续教育状况研究

中共中央、国务院印发的《新时期产业工人队伍建设改革方案》提出,要帮助"新生代农民工增加受教育培训机会,提高专业技能和胜任岗位能力",并将教育作为促进农民工融入城市,成为稳定就业的产业工人的重要路径。以浙江省11个地级市的服务行业、建筑行业、制造业的从业人员为主要调查对象,了解浙江省新生代农民工接受继续教育的现状与特点,发现新生代农民工存在自身接受继续教育的动力意愿不足、企业进行职业技能培训投资的动力意愿不足等问题,并据此提出要做好终身教育宣传、搭建继续教育平台、优化在职培训模式等解决办法。     

Accurate estimation for extra-Poisson variability assuming random effect models

In this study, the components of extra-Poisson variability are estimated assuming random effect models under a Bayesian approach. A standard existing methodology to estimate extra-Poisson variability assumes a negative binomial distribution. The obtained results show that using the proposed random effect model it is possible to get more accurate estimates for the extra-Poisson variability components when compared to the use of a negative binomial distribution where it is possible to estimate only one component of extra-Poisson variability. Some illustrative examples are introduced considering real data sets.     

Testing the dilation order by using cumulative residual Tsallis entropy

In this paper, we introduce a new test for the dilation order based on cumulative residual Tsallis entropy of order α. The effect of the values of parameter α on the power of the test statistics is numerically investigated. The asymptotic distribution of the test statistic is given. The performance of the test statistic is evaluated using a simulation study. Finally, some numerical examples illustrating the theory are also given.     

Noninformative priors for the between-group variance in the unbalanced one-way random effects model with heterogeneous error variances

For the unbalanced one-way random effects model with heterogeneous error variances, we propose the non-informative priors for the between-group variance and develop the first- and second-order matching priors. It turns out that the second-order matching priors do not exist and the reference prior and Jeffreys prior do not satisfy a first-order matching criterion. We also show that the first-order matching prior meets the frequentist target coverage probabilities much better than the Jeffreys prior and reference prior through simulation study, and the Bayesian credible intervals based on the matching prior and reference prior give shorter intervals than the existing confidence intervals by examples.     

Linear scalar-on-surface random effects regression models

Many research fields increasingly involve analyzing data of a complex structure. Models investigating the dependence of a response on a predictor have moved beyond the ordinary scalar-on-vector regression. We propose a regression model for a scalar response and a surface (or a bivariate function) predictor. The predictor has a random component and the regression model falls in the framework of linear random effects models. We estimate the model parameters via maximizing the log-likelihood with the ECME (Expectation/Conditional Maximization Either) algorithm. We use the approach to analyze a data set where the response is the neuroticism score and the predictor is the resting-state brain function image. In the simulations we tried, the approach has better performance than two other approaches, a functional principal component regression approach and a smooth scalar-on-image regression approach.     

Lower bound of average centered L2-discrepancy for U-type designs

Uniform designs are widely used in various scientific investigations and industrial applications. By considering all possible level permutation of the factors, a connection between average centered L₂-discrepancy and generalized wordlength pattern for asymmetrical fractional factorial designs is derived. Moreover, we present new lower bounds to the average centered L₂-discrepancy for symmetrical and asymmetrical U-type designs. For illustration of the theoretical results, the lower bounds for symmetrical and asymmetrical U-type designs are tabulated, and numerical results indicate that our lower bounds behave well and can be recommended for use in practice.     

A semiparametric approach for modelling multivariate nonlinear time series

In this article, a semiparametric time‐varying nonlinear vector autoregressive (NVAR) model is proposed to model nonlinear vector time series data. We consider a combination of parametric and nonparametric estimation approaches to estimate the NVAR function for both independent and dependent errors. We use the multivariate Taylor series expansion of the link function up to the second order which has a parametric framework as a representation of the nonlinear vector regression function. After the unknown parameters are estimated by the maximum likelihood estimation procedure, the obtained NVAR function is adjusted by a nonparametric diagonal matrix, where the proposed adjusted matrix is estimated by the nonparametric kernel estimator. The asymptotic consistency properties of the proposed estimators are established. Simulation studies are conducted to evaluate the performance of the proposed semiparametric method. A real data example on short‐run interest rates and long‐run interest rates of United States Treasury securities is analyzed to demonstrate the application of the proposed approach. The Canadian Journal of Statistics 47: 668–687; 2019 © 2019 Statistical Society of Canada     

新生代农民工的城市适应研究

在城市适应的过程中,新生代农民工既要接受现代文明的观念,又要了解城市的各种规则,他们对前者的接受要高于对后者的了解。在心理和精神层面上,新生代农民工很接近城市文明,但是因为制度设置的欠缺,他们被排除在城市社会生活之外;同时,城市也缺乏对农民工有针对性的宣传、教育,这使农民工们对与自己利益有关的法律、法规的了解和认识不够,无法较好地保护自己的权益。     

新生代农民工市民化困境与政策选择

新生代农民工市民化是社会经济发展的迫切需要,是解决农民工问题的根本出路.然而,我国新生代农民工市民化进程却面临诸多困境,主要是制度安排困境、社会资本困境和人力资本困境三个方面.新生代农民工市民化的出路在于改革和创新制度、培育社会资本和提升人力资本.