Semiparametric ROC surface estimation for continuous diagnostic tests via polytomous logistic regression procedures

In this paper, we propose a semiparametric method of estimating receiver operating characteristic (ROC) surfaces for continuous diagnostic tests under density ratio models. Implementation of our method is easy since the usual polytomous logistic regression procedures in many statistical software packages can be employed. A simulated example is provided to facilitate the implementation of our method. Simulation results show that the proposed semiparametric ROC surface estimator is more efficient than the nonparametric counterpart and the parametric counterpart whether the normality assumption of data holds or not. Moreover, some simulation results on the underlying semiparametric distribution function estimators are also reported. In addition, some discussions on the proposed method as well as analysis of a real data set are provided.     

Confidence Intervals for the Youden Index and Corresponding Optimal Cut-Point

A global measure of biomarker effectiveness is the Youden index, the maximum difference between sensitivity, the probability of correctly classifying diseased individuals, and 1-specificity, the probability of incorrectly classifying healthy individuals. The cut-point leading to the index is the optimal cut-point when equal weight is given to sensitivity and specificity. Using the delta method, we present approaches for estimating confidence intervals for the Youden index and corresponding optimal cut-point for normally distributed biomarkers and also those following gamma distributions. We also provide confidence intervals using various bootstrapping methods. A comparison of interval width and coverage probability is conducted through simulation over a variety of parametric situations. Confidence intervals via delta method are shown to have both closer to nominal coverage and shorter interval widths than confidence intervals from the bootstrapping methods.     

Does biomass energy consumption reduce total energy CO2 emissions in the US?

This study investigates the causal relationship between total biomass energy consumption, total energy CO₂ emissions, and GDP in the United States for the period January 1973–December 2016 by employing a directed acyclic graph (DAG) techniques and the Autoregressive Distributed Lag (ARDL) model. Additionally, this paper examines the existence of the environmental Kuznets curve (EKC) hypothesis. The result of the DAG reveals that total biomass energy consumption and GDP have a unidirectional contemporaneous causal relationship with total energy CO₂ emissions. Based on the results of the ARDL, we find that a 1% increase in per capita total biomass energy consumption causes a 0.65% reduction in per capita total energy CO₂ emissions in the long-run. This finding implies that expanding the usage of biomass is one way to reduce and control greenhouse gases in the US. Moreover, we find that the inverted U-shaped EKC hypothesis is satisfied for the US case. Findings from this study suggest that energy policies should stimulate an increase of biomass production for reducing total energy CO₂.     

汽轮发电机转子静挠度的多元统计分析

讨论了如何用多元线性回归分析的统计方法与有限元法相结合,计算发电机组转子的热应变、基础负载沉降及支承预诸因素影响下的静挠曲线。     

小型回转点云的配准研究

针对回转半径较大的小型回转点云配准时其回转特征不明显问题,提出一种先利用最小二乘法计算出点云的拟合平面并将所有点投影到该平面上,再利用滚球算法计算该二维点云数据的边界,并对边界点云进行3次B样条拟合,最后根据B样条曲线的变化特点,找出特征点,并利用特征点进行配准。对于回转半径较大且尺寸较小的回转点云,由 于其回转特性不是非常明显,因此完全依靠其回转特性进行配准,会有一定的难度,所以需要利用其他一些特征进行配准。最后通过三坐标扫描仪获取物体的外表面形状数据,然后进行配合验证,结果表明可以准确获取特征点。该方法可以有效地对小型点云进行配准。     

考虑柔性和间隙的螺旋引纬机构仿真分析

为了提高引纬机构的稳定性和精度,将柔性构件和间隙的影响考虑在内,联合ANSYS和ADAMS建立螺旋引纬机构的刚柔耦合模型,通过ADAMS进行动力学仿真,在刚性系统和刚柔耦合系统中分别分析了旋转铰间隙对剑杆输出特性曲线的影响。在对仿真数据的观察中发现了相当比例的噪声信号,为了消除噪声信号的影响,利用统计学中上下四分位数和中位数来识别替换异常值;在综合考虑间隙和柔性构件的影响时,利用减噪后的加速度曲线与理想刚体系统下的数据的偏差值得到一个新的曲线,该曲线在固定区间内做高频波动,呈现出类似周期性声波信号的特点。结果表明旋转铰间隙是影响剑杆稳定性和精度的主要因素,柔性构件和间隙的综合作用产生了趋向于周期性的碰撞影响。依据研 究结果可以进一步对原刚体模型下设计的引纬机构进行优化设计。     

曲线、曲面的可控修形研究

目前,曲线、曲面的计算机辅助设计已广泛地应用于工程实际中,但是对已生成的曲线、曲面进行修改的研究还较少。文章针对现存软件的不足,研究了曲线、曲面的修改。通过修改曲线、曲面的参数,可快速得到所期望的曲线、曲面形状。此研究是用C语言实现其算法的。     

Constants and variables: Pareto's asymmetric distribution curve

The article sets out the classic Paretian theory of income distribution. As it does so, it seeks to highlight the constant elements in the human faculties represented, at aggregate level, by the invariability and persistence of the asymmetric income curve, and the variable elements connected with the same curve and manifest in upward and downward mobility – what Pareto calls ‘circulation’. The two theorems arising from Pareto's discovery constitute a specific theory of development which has been confirmed from two points of view: in the positive sense of development when – as in the West – the second theorem has been applied (also independently of Pareto) with the connected theory of the entrepreneur and innovation in democratic regimes; but also in the negative sense of underdevelopment, with the destruction of wealth and the consequent general impoverishment, when expropriation policies have been implemented in despotic regimes, or excessive taxation in others.     

Nonparametric Kernel Methods with Errors‐in‐Variables: Constructing Estimators,Computing them,and Avoiding Common Mistakes

Estimating a curve nonparametrically from data measured with error is a difficult problem that has been studied by many authors. Constructing a consistent estimator in this context can sometimes be quite challenging, and in this paper we review some of the tools that have been developed in the literature for kernel‐based approaches, founded on the Fourier transform and a more general unbiased score technique. We use those tools to rederive some of the existing nonparametric density and regression estimators for data contaminated by classical or Berkson errors, and discuss how to compute these estimators in practice. We also review some mistakes made by those working in the area, and highlight a number of problems with an existing R package decon .