未焊透缺陷安全尺寸的评价研究

未焊透是压力管道焊接接头常见缺陷之一,严重时会导致管道的承载能力明显降低,进而引发严重的安全事故。 目前我国的《在用工业管道定期检验规程》中对未焊透管道的定级是很保守的,为了减少对未焊透缺陷的返修,文章采 用非线性有限元分析的方法,通过简化未焊透管道的影响因素,并研究在不同载荷工况下管道失效模式与缺陷尺寸的关 系,获得了未焊透管道的极限载荷曲线簇;再运用塑性力学分析方法对有限元分析结果进行筛选,得到了焊接缺陷的安 全尺寸;最终通过试验验证极限载荷数值分析结果的合理性。研究结果表明含有超标焊接缺陷（如未焊透）的管道不通 过复杂应力计算和安全评定即可在保证安全前提下最大限度地使用。     

Robust inference for estimating equations with nonignorably missing data based on SIR algorithm

Nonresponse is a very common phenomenon in survey sampling. Nonignorable nonresponse – that is, a response mechanism that depends on the values of the variable having nonresponse – is the most difficult type of nonresponse to handle. This article develops a robust estimation approach to estimating equations (EEs) by incorporating the modelling of nonignorably missing data, the generalized method of moments (GMM) method and the imputation of EEs via the observed data rather than the imputed missing values when some responses are subject to nonignorably missingness. Based on a particular semiparametric logistic model for nonignorable missing response, this paper proposes the modified EEs to calculate the conditional expectation under nonignorably missing data. We can apply the GMM to infer the parameters. The advantage of our method is that it replaces the non-parametric kernel-smoothing with a parametric sampling importance resampling (SIR) procedure to avoid nonparametric kernel-smoothing problems with high dimensional covariates. The proposed method is shown to be more robust than some current approaches by the simulations.     

On hysteretic vector autoregressive model with applications

This paper proposes a new hysteretic vector autoregressive (HVAR) model in which the regime switching may be delayed when the hysteresis variable lies in a hysteresis zone. We integrate an adapted multivariate Student-t distribution from amending the scale mixtures of normal distributions. This HVAR model allows for a higher degree of flexibility in the degrees of freedom for each time series. We use the proposed model to test for a causal relationship between any two target time series. Using posterior odds ratios, we overcome the limitations of the classical approach to multiple testing. Both simulated and real examples herein help illustrate the suggested methods. We apply the proposed HVAR model to investigate the causal relationship between the quarterly growth rates of gross domestic product of United Kingdom and United States. Moreover, we check the pairwise lagged dependence of daily PM2.5 levels in three districts of Taipei.     

FEPA-金融时间序列自适应组合预测模型

本文报告一种金融时间序列预测的信号分析、信息融合与智能计算组合模型,简称FEPA,由针对金融时间序列（FTS）信号分析的经验模态分解（EMD）、用于数据降维的主成分分析（PCA）和用于非线性建模的人工神经网络（ANN）三部分组成。该模型首先应用滑动窗口截取原始金融时间序列最近期数据集,应用EMD分解算法把数据集分解成不同尺度的本征模态函数（IMF）,然后通过主成分分析将分解后的数据降维,提取最有信息量的特征;然后将这些特征输入到神经网络进行组合预测。本文提出的组合预测模型FEPA是基于分解-提优-合成的信息融合思想,有效提高了预测可靠性。其创新点在于：1）首次给出了EMD算法的结构化表达,提供了今后融合更多信息的算法接口;2）通过多步长预测输出深入研究EMD分解的有效信息结构;3）通过切换到更细时间框架来处理EMD的端点效应,并探索了两级时间框架下的预测效果;4）给出了金融时间序列组合预测模型的一般性架构,具有可升级性和可扩展性。并且通过滑动窗口EMD使得实证更能切近实际。通过在沪深300股指和澳大利亚股指上的实证,结果表明FEPA预测模型在沪深300股指日线和15分钟线上的预测命中率高达78%和82%,在澳大利亚股指日线上也达到了74%的命中率,经比较,明显高于文献中常见的5种模型。     

绿色增长下的导向性技术创新选择研究

针对如何正确引导 GHG 排放的气候反馈经济损失下导向性技术创新,以实现长期的绿色增长这一问题,基于 DICE 模型,重构导向性技术创新不同的动力要素及其与气候变化之间的内生关系,建立了可体现绿色增长“均衡性”、“包容性”和“可持续性”的非线性最优控制模型。数值仿真结果显示: 气候反馈经济损失不容小觑; 偏于生产的绿色技术创新导向下社会生产力强劲但存在气候环境恶化风险,偏于减排的绿色技术创新导向下气候反馈经济损失小但存在增长动力匮乏风险,二者虽均可实现长期的绿色增长,但中性的绿色技术创新导向会更稳妥; 偏于生产的绿色技术创新导向下人均消费变化率在短期内会显著提升。     

ESTIMATING A PARAMETER WHEN IT IS KNOWN THAT THE PARAMETER EXCEEDS A GIVEN VALUE

In some statistical problems a degree of explicit, prior information is available about the value taken by the parameter of interest, θ say, although the information is much less than would be needed to place a prior density on the parameter's distribution. Often the prior information takes the form of a simple bound, ‘θ > θ₁ ’ or ‘θ < θ₁ ’, where θ₁ is determined by physical considerations or mathematical theory, such as positivity of a variance. A conventional approach to accommodating the requirement that θ > θ₁ is to replace an estimator, , of θ by the maximum of and θ₁. However, this technique is generally inadequate. For one thing, it does not respect the strictness of the inequality θ > θ₁ , which can be critical in interpreting results. For another, it produces an estimator that does not respond in a natural way to perturbations of the data. In this paper we suggest an alternative approach, in which bootstrap aggregation, or bagging, is used to overcome these difficulties. Bagging gives estimators that, when subjected to the constraint θ > θ₁ , strictly exceed θ₁ except in extreme settings in which the empirical evidence strongly contradicts the constraint. Bagging also reduces estimator variability in the important case for which is close to θ₁, and more generally produces estimators that respect the constraint in a smooth, realistic fashion.     

Non-asymptotic sequential confidence regions with fixed sizes for the multivariate nonlinear parameters of regression

In this paper we consider a sequential design for the estimation of nonlinear parameters of regression with guaranteed accuracy. Non-asymptotic confidence regions with fixed sizes for the least squares estimates are used. The obtained confidence region is valid for finite numbers of data points when the distributions of the observations are unknown.     

从“凯恩斯效应”到“阿米曲线”——政府规模与经济增长关系的非线性转向

政府规模与经济增长之间的关系问题是宏观经济研究中的核心问题之一.关于两者关系的传统研究往往以线性假定为基础,所以实证结果出现了巨大的差异.这种现象促使研究者从非线性角度重新审视政府规模与经济增长之间的关系.“巴罗法则”在内生增长框架内为政府规模与经济增长关系研究提供了一个标准的分析框架,揭示了两者之间的倒“U”关系.而“Armey曲线”不仅非常直观地表达了政府规模与经济增长之间的非线性关系,同时还指出这种非对称关系的非对称性质,具有很强的总结性和政策指导意义.     

一种基于小波变换和PCA的人脸识别改进方法

针对主成分分析PCA算法中存在的问题,提出了利用小波变换对人脸图像进行预处理,PCA提取图像特征,最近邻法分类的人脸识别改进方法.基于ORL、YALE数据库的相关实验表明,这样的系统能够降低数据维数和克服角度、位移变化对算法的影响,能够取得比传统PCA更好的识别性能。     

信贷规模与房地产价格的非线性动态关系——基于中国直辖市数据的比较研究

运用非线性平滑转换模型对中国直辖市的信贷规模与房地产价格的动态关系进行对比研究。研究发现如果想要通过控制信贷规模调控房地产价格,天津和重庆更容易达成调控目标,北京则很难。在信贷规模对房地产价格产生非线性影响的条件下,信贷规模对房地产价格的影响存在不同状态,重庆在不同状态间的变化速度最快,其次是天津,最慢的是北京。