稳压电源的自适应去火弧技术

针对传统基于继电器控制的稳压电源在带负载工作时,档位切换常常伴随火弧现象的问题,设计了一种可广泛 应用于此类稳压电源上的自适应去火弧技术：使用光耦和多级运放完成对零点和电压电流的采样;基于单片机的10量, 实现对继电器的驱动和控制;基于RCC自激震荡原理为系统提供稳定电源;设计了稳压器的完整软件系统程序,并在软 件中应用自适应去火弧算法。实验结果表明：自适应去火弧技术,能够有效地规避火弧现象的产生,并能够根据条件作 出调整。采用自适应去火弧技术的稳压系统达到了设计目标,延长了系统工作寿命。     

农地抵押融资意愿与最优土地规模

运用2014年陕西、宁夏两省2493户农户的调研数据,采用Probit模型,以农地规模对农户参与农地抵押融资意愿的影响分析为基础,实证分析农户参与农地抵押融资意愿最大化条件下的最优土地规模。研究发现：农地规模对农户参与农地抵押融资意愿呈倒U型影响,高陵、平罗地区农户参与农地抵押融资的最优土地规模分别为12.67亩、56.50亩,分别是当地户均农地规模的2.68倍、3.00倍,农业主导产业差异导致最优土地规模有所不同。总体看来,样本农户的家庭规模、经营类型、往返金融机构的便利程度、金融机构数量对农户参与农地抵押融资意愿有较显著的影响,而分区域来看,各控制变量的影响效应存在较大区别。     

改进离散人工蜂群算法规划异形满版服饰图案切割路径

服饰图案切割路径规划是服饰图案自动切割技术的关键部分之一。文中用改进的离散人工蜂群算法实现了激 光切割系统对异形满版服饰图案的切割工艺路径规划优化。首先,把满版服饰图案激光切割路径规划转化为广义旅行 商问题求解,证明了路径规划转化成普通TSP问题比转化成第一类广义TSP问题更为有效,并给出了路径规划中“图案 始切割点”的优化选择方法。然后结合部分匹配交叉和启发式变异搜索方法的人工蜂群算法求解TSP问题,证明了与 A*算法及遗传算法相比,该方法更为优越。并且该方法已应用于激光切割机切割异形满版服饰图案路径规划的生产实 践,运行可靠,比未规划时的工艺路线平均提高效率33.7%。该方法提供了一种解决TSP问题的新方法,具有一定的理 论意义和价值     

Subgroup Analysis with Time‐to‐Event Data Under a Logistic‐Cox Mixture Model

Subgroup detection has received increasing attention recently in different fields such as clinical trials, public management and market segmentation analysis. In these fields, people often face time‐to‐event data, which are commonly subject to right censoring. This paper proposes a semiparametric Logistic‐Cox mixture model for subgroup analysis when the interested outcome is event time with right censoring. The proposed method mainly consists of a likelihood ratio‐based testing procedure for testing the existence of subgroups. The expectation–maximization iteration is applied to improve the testing power, and a model‐based bootstrap approach is developed to implement the testing procedure. When there exist subgroups, one can also use the proposed model to estimate the subgroup effect and construct predictive scores for the subgroup membership. The large sample properties of the proposed method are studied. The finite sample performance of the proposed method is assessed by simulation studies. A real data example is also provided for illustration.     

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.     

Symmetrical Design of Experiment in Global Sensitivity Analysis Based on ANOVA High-dimensional Model Representation

This article reviews symmetrical global sensitivity analysis based on the analysis of variance of high-dimensional model representation. To overcome the computational difficulties and explore the use of symmetrical design of experiment (SDOE), two methods are presented. If the form of the objective function f is known, we use SDOE to estimate the symmetrical global sensitivity indices instead of Monte Carlo or quasi-Monte Carlo simulation. Otherwise, we use the observed values of the experiment to do symmetrical global sensitivity analysis. These methods are easy to implement and can reduce the computational cost. An example is given by symmetrical design of experiment.     

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.     

Bayesian estimation and influence diagnostics of generalized partially linear mixed-effects models for longitudinal data

This paper develops a Bayesian approach to obtain the joint estimates of unknown parameters, nonparametric functions and random effects in generalized partially linear mixed models (GPLMMs), and presents three case deletion influence measures to identify influential observations based on the φ-divergence, Cook's posterior mean distance and Cook's posterior mode distance of parameters. Fisher's iterative scoring algorithm is developed to evaluate the posterior modes of parameters in GPLMMs. The first-order approximation to Cook's posterior mode distance is presented. The computationally feasible formulae for the φ-divergence diagnostic and Cook's posterior mean distance are given. Several simulation studies and an example are presented to illustrate our proposed methodologies.     

Stochastic analysis of covariance when the error distribution is long-tailed symmetric

In this study, we consider stochastic one-way analysis of covariance model when the distribution of the error terms is long-tailed symmetric. Estimators of the unknown model parameters are obtained by using the maximum likelihood (ML) methodology. Iteratively reweighting algorithm is used to compute the ML estimates of the parameters. We also propose new test statistic based on ML estimators for testing the linear contrasts of the treatment effects. In the simulation study, we compare the efficiencies of the traditional least-squares (LS) estimators of the model parameters with the corresponding ML estimators. We also compare the power of the test statistics based on LS and ML estimators, respectively. A real-life example is given at the end of the study.     

基于可信性理论的Mean-CVaR投资组合优化

选取CVaR作为风险度量指标,在可信性理论的基础上构建Mean-CVaR投资组合模型,采用Markov过程预测作为模糊变量的预期投资收益率,并设计基于模糊模拟和遗传算法的混合智能算法以求解;选取上证50成份股2013—2014年的日度历史交易数据,将该模型应用到中国证券市场,结果发现该投资组合模型与中国证券市场的环境具有一定的适应性,能够为投资者的投资决策提供依据。