基于学分绩点与综合素质测评的换算模型建立

学生综合素质测评体系的完善对有效实施大学生综合素质测评工作以及指导大学生健康发展有重要意义,其中,学生综合素质测评体系的关键在于学分绩点与综合素质测评的换算模型的转化。通过分析高校现行学分绩点测评公式,发现其存在的主要缺陷是转化公式不满足统计学原理,并指出学分绩点测评的换算公式应建立在对特定学生群体特征参数群数值分析的基础上。以西南石油大学某专业学生某学年的成绩分布数据进行验证,采取统计原理、常数变异法和不动点原理进行建模,利用求导方法对学分绩点测评中的特征参数群进行层次划分,然后对每段划分积分,通过归一化来确定变异项,得出新的换算模型。结果表明新模型能更好地反映学生的学习成绩在综合素质测评中的地位。     

步进电机细分驱动系统设计

为了提高步进电机的运行精度,消除或减小振动现象,文章基于细分驱动技术,以单片机ATmega128为核心,应用恒流斩波PWM发生电路和H桥功率驱动电路设计了步进电机的驱动系统。系统控制参数通过上位机设定,以串口通信传给单片机。实验结果表明：电机运行平稳,振动噪音小,转矩大,精度高,系统具有良好的应用前景。     

Visualization for Large‐scale Gaussian Updates

In geostatistics and also in other applications in science and engineering, it is now common to perform updates on Gaussian process models with many thousands or even millions of components. These large‐scale inferences involve modelling, representational and computational challenges. We describe a visualization tool for large‐scale Gaussian updates, the ‘medal plot’. The medal plot shows the updated uncertainty at each observation location and also summarizes the sharing of information across observations, as a proxy for the sharing of information across the state vector (or latent process). As such, it reflects characteristics of both the observations and the statistical model. We illustrate with an application to assess mass trends in the Antarctic Ice Sheet, for which there are strong constraints from the observations and the physics.     

Robust Inference for Near-Unit Root Processes with Time-Varying Error Variances

The autoregressive Cauchy estimator uses the sign of the first lag as instrumental variable (IV); under independent and identically distributed (i.i.d.) errors, the resulting IV t-type statistic is known to have a standard normal limiting distribution in the unit root case. With unconditional heteroskedasticity, the ordinary least squares (OLS) t statistic is affected in the unit root case; but the paper shows that, by using some nonlinear transformation behaving asymptotically like the sign as instrument, limiting normality of the IV t-type statistic is maintained when the series to be tested has no deterministic trends. Neither estimation of the so-called variance profile nor bootstrap procedures are required to this end. The Cauchy unit root test has power in the same 1/T neighborhoods as the usual unit root tests, also for a wide range of magnitudes for the initial value. It is furthermore shown to be competitive with other, bootstrap-based, robust tests. When the series exhibit a linear trend, however, the null distribution of the Cauchy test for a unit root becomes nonstandard, reminiscent of the Dickey-Fuller distribution. In this case, inference robust to nonstationary volatility is obtained via the wild bootstrap.     

儒家的社会规范与阴阳五行秩序

所谓规范指称"标准化的秩序"。社会规范指的是为社会成员所提供的是非标准和行为指南,因而也是人们具体行为必须要遵循的行为法则。儒家的社会规范可分为两种:一是以"德礼"为核心的积极的规范,其目的在于通过教化的力量来改变人们行为的根本态度;二是以"法刑"为核心的消极规范,其目的在于通过物理的制裁来暂时控制人们的行为。规范必须拥有其合理性。在儒家看来,社会的规范不是来自于近代社会学理论所讲的社会契约,而是来自于自然本身的秩序。因而"三纲五伦"以及"礼乐"、"刑政"等规范都体现着阴阳五行的自然法则。跟近代的社会学理论相比,儒家的社会规范具有以下的特点:其一,儒家的社会规范不是以平等的个人,而是以有差别的个人为前提;其二,儒家规范的根本目的不在于实现功利性或者效率性,而在于实现道德性;其三,儒家的规范不是为了实现社会契约,而是为了实现阴阳五行的自然秩序。     

A Weighted Least Squares Approach to Levene's Test of Homogeneity of Variance

In 1960 Levene suggested a potentially robust test of homogeneity of variance based on an ordinary least squares analysis of variance of the absolute values of mean-based residuals. Levene's test has since been shown to have inflated levels of significance when based on the F-distribution, and tests a hypothesis other than homogeneity of variance when treatments are unequally replicated, but the incorrect formulation is now standard output in several statistical packages. This paper develops a weighted least squares analysis of variance of the absolute values of both mean-based and median-based residuals. It shows how to adjust the residuals so that tests using the F -statistic focus on homogeneity of variance for both balanced and unbalanced designs. It shows how to modify the F -statistics currently produced by statistical packages so that the distribution of the resultant test statistic is closer to an F-distribution than is currently the case. The weighted least squares approach also produces component mean squares that are unbiased irrespective of which variable is used in Levene's test. To complete this aspect of the investigation the paper derives exact second-order moments of the component sums of squares used in the calculation of the mean-based test statistic. It shows that, for large samples, both ordinary and weighted least squares test statistics are equivalent; however they are over-dispersed compared to an F variable.     

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.     

线性可调恒流大功率LED驱动电源设计

为满足大功率LED驱动的性能要求,如驱动能力强、效率高、稳定可靠、可线性调节等,本研究设计一种线性可调恒流驱动电源方案.该电路由DC-DC转换模块、恒流驱动模块、PWM数模转换组成.其中,DC-DC转换模块以TPS51113为核心,采用大功率MOSFET同步BUCK模式;恒流驱动模块采用运放加MOSFET典型电路,实现...     

Variance estimation based on blocked 3×2 cross-validation in high-dimensional linear regression

In high-dimensional linear regression, the dimension of variables is always greater than the sample size. In this situation, the traditional variance estimation technique based on ordinary least squares constantly exhibits a high bias even under sparsity assumption. One of the major reasons is the high spurious correlation between unobserved realized noise and several predictors. To alleviate this problem, a refitted cross-validation (RCV) method has been proposed in the literature. However, for a complicated model, the RCV exhibits a lower probability that the selected model includes the true model in case of finite samples. This phenomenon may easily result in a large bias of variance estimation. Thus, a model selection method based on the ranks of the frequency of occurrences in six votes from a blocked 3×2 cross-validation is proposed in this study. The proposed method has a considerably larger probability of including the true model in practice than the RCV method. The variance estimation obtained using the model selected by the proposed method also shows a lower bias and a smaller variance. Furthermore, theoretical analysis proves the asymptotic normality property of the proposed variance estimation.