Goodness-of-fit tests for multi-dimensional copulas:Expanding application to historical drought data

The question of how to choose a copula model that best fits a given dataset is a predominant limitation of the copula approach, and the present study aims to investigate the techniques of goodness-of-fit tests for multi-dimensional copulas. A goodness-of-fit test based on Rosenblatt＇s transformation was mathematically expanded from two dimensions to three dimensions and procedures of a bootstrap version of the test were provided. Through stochastic copula simulation, an empirical application of historical drought data at the Lintong Gauge Station shows that the goodness-of-fit tests perform well, revealing that both trivariate Gaussian and Student t copulas are acceptable for modeling the dependence structures of the observed drought duration, severity, and peak. The goodness-of-fit tests for multi-dimensional copulas can provide further support and help a lot in the potential applications of a wider range of copulas to describe the associations of correlated hydrological variables. However, for the application of copulas with the number of dimensions larger than three, more complicated computational efforts as well as exploration and parameterization of corresponding copulas are required.

Frequency Domain Goodness of Fit Test Based Spectrum Sensing Method with Dynamically Varying Noise

In cognitive radio networks,spectrum sensing under circumstances of dynamically varying noise and lacking prior information is a key challenge to the conventional spectrum sensing algorithms.Since the necessary information is rather difficult to obtain practically,most existing spectrum sensing methods are fettered in applications.Motivated by these,in this paper,a Frequency domain Goodness of Fit Test(FGoF)based spectrum sensing method is proposed.The FGoF makes full use of underlying information in Guard-Bands and the advantages of GoF test works for any distribution.Analytical and simulated results show that the FGoF is a robust spectrum sensing method in cognitive radio with the inherent advantages of invulnerability to dynamically varying noise.

Asymptomatic Distribution of Goodness-of-Fit Tests in Logistic Regression Model

The logistic regression model has been become commonly used to study the association between a binary response variable;it is widespread application rests on its easy application and interpretation. The subject of assessment of goodness-of-fit in logistic regression model has attracted the attention of many scientists and researchers. Goodness-of-fit tests are methods to determine the suitability of the fitted model. Many of methods proposed and discussed for assessing goodness-of fit in logistic regression model, however, the asymptotic distribution of goodness-of-fit statistics are less examine, it is need more investigated. This work, will focus on assessing the behavior of asymptotic distribution of goodness-of-fit tests, also make comparison between global goodness-of-fit tests, and evaluate it by simulation.

用不同方法评估山西地震台网的监测能力

基于山西数字地震台网2015—2019年记录的地震目录及观测报告数据,采用“基于概率的完整性震级”PMC方法、震级—序号法、最优拟合度(GFT)法和最大曲率法等多种方法分别评估山西地震台网的监测能力。结合山西测震台网的实际情况和不同评估方法的计算原理进行对比分析,结果显示,部分台站围限的区域能监测到M L1.0及以下的地震外,其余区域的监测能力下限为M L1.5;“基于概率的完整性震级”方法更适合用于评估山西地震台网的监测能力。随着预警台网的正式运行,建议采用该方法对地震预警台网的实际监测能力重新评估。

基于机场跑道饱和度的空中交通阻抗建模

通过对空中交通阻抗基本要素的解析,参考道路交通BPR(Bureau of Public Road)阻抗函数,提出一种综合考虑起飞机场和降落机场跑道饱和度的空中交通阻抗函数模型以研究空中交通阻抗问题,为空中交通阻抗分析提供新的研究思路。采用南京禄口机场-北京首都机场的航班数据进行拟合分析,对模型参数进行标定。根据有无流量控制分别给出了仅考虑降落机场跑道饱和度模型的拟合结果并进行了参数检验,同时进一步与综合考虑起降机场跑道饱和度模型的拟合优度指标进行对比分析。结果显示:综合模型在无流控情况下精度提升了11.9%,有流控情况下精度提升了16.7%,可以为空中交通管理提供参考。

“多碰头”复合灾害遭遇组合概率及重现期计算方法研究

为揭示变化环境下“多碰头”复合水文地质灾害的风险规律,采用二维Copula联合概率函数,构建具有“藤状”结构的复合灾害遭遇组合概率分布模型,对模型进行精确拟合,选用log-lik、AIC、BIC三种拟合优度检验方法进行优选。针对不同灾害发生情景,计算复合灾害遭遇组合的联合累积概率及肯德尔联合重现期。以粤港澳大湾区佛山站(降雨-滑坡(泥石流)-山洪)以及灯笼山站(台风-风暴潮增水-洪水)复合灾害遭遇组合事件为例,构建三类灾害组合的概率分布模型。结果表明,随着单一灾害情景的极端性增加,复合灾害遭遇组合联合累积概率及重现期相应增大,从而揭示了复合灾害的风险模式。本研究可为预估“多碰头”复合灾害遭遇组合的风险提供理论支持,对于复合水文地质灾害风险预估有参考意义。

基于呼吸机传感器系统故障关联的失效率和可靠性研究

目的 针对呼吸机使用过程中存在的故障率高、危害性强等问题,分析呼吸机传感器系统故障关联,探究呼吸机传感器系统的失效率和可靠性,为呼吸机预防性维护提供参考。方法 选取2010—2022年间我院及原厂工程师提供的单一型号呼吸机传感器系统相关维修工单为研究对象,使用伽马分布模型和参数估计方法,选取拟合优度检验、K-S检验,拟合优度评价指标包括均方误差、均方根误差、平均绝对误差对模型进行验证分析。结果 呼吸机6种故障点K-S检验统计量D值分别为1.99×10^(-2)、2.98×10^(-2)、4.03×10^(-2)、4.06×10^(-2)、3.13×10^(-2)、6.22×10^(-2),拟合优度检验统计量χ2分别为396.95、601.01、23.25、13.94、52.35、5.55。除气体混合压力传感器故障外,各故障点模型计算值与实际值分布相同,差异具有统计学意义(P<0.05)。6种故障点平均故障间隔时间、可靠性均值时间以及极值时间均与实际结果基本保持一致。结论 基于伽马理论的故障分布模型,可对呼吸机传感器系统的故障分布、失效率以及可靠性进行有效分析,为制定有针对性的预防性维护方案提供循证依据。

基于Flexsim的急诊科就诊流程仿真与优化

如何缩短患者就诊时间,提高服务效率和质量已经成为医院急诊科亟待解决的重要问题。基于Flexsim软件建模,对急诊科就诊流程进行仿真与优化。首先,结合Kolmogorov-Smirnov拟合优度检验等数理统计相关方法,对仿真模型所需的输入数据进行处理;然后,通过Flexsim软件建模仿真,找出急诊科就诊流程的资源瓶颈环节;最后,提出优化改进方案,以消除资源瓶颈,并基于Flexsim仿真模型,证实优化方案的合理性。通过优化改进,消除了急诊科系统的资源瓶颈,有效减少了患者拥挤现象,缩短了就诊时间,提高了就诊效率和患者满意度。

C.TEST语言能力的验证性因素分析

本文根据前期探索性因素分析成果和相关语言测试理论,提出了两个C.TEST语言能力结构假设模型,分别是C.TEST(A～D级)模型1和C.TEST(E～F级)模型1。然后,使用LISREL统计软件分别对两个假设模型和两个对比模型进行了验证性因素分析。统计数据表明:本研究中提出的两个假设模型都能较好地拟合数据,且优于两个对比模型,这说明研究中提出的两个假设模型是合理的,也说明C.TEST的确具有较高的构想效度。

兴安盟暴雪时空变化特征及重现期分析

暴雪是兴安盟最主要的灾害性天气之一,对区域性暴雪的统计分析能够为生产生活、城镇建设、交通运输等人类活动提供高信度且有针对性的参考信息。文章根据1981—2022年兴安盟8个国家气象站的降雪量、积雪深度、天气现象等历史气象要素,基于耿贝尔极值分布推算出不同重现期下的极端降雪量。结果表明:(1)兴安盟暴雪日数存在从逐渐减少到基本持平的年代际变化特征,年降雪量极值和站点比例在相应时间段表现出较为一致的变化趋势,21世纪10年代以来极端降雪事件发生频繁,极端性特征显著。多站点在2020年前后存在较为显著的降雪极值震荡,均在这一时期达到或接近降雪极值。(2)最大日降雪量较高的区域集中在兴安盟海拔高度较高的北部和西北部地区。最大日降雪量和暴雪总日数的空间分布特征极为相似,均表现出北多南少、北高南低的特征。(3)兴安盟南部各气象站点在不同重现期下的降雪极值相对较小;北部以及西北部降雪强度较高,风险相对较高,需关注极端灾害性天气的可能影响及风险。

结合KSTEST统计检验方法的联合像素时序干涉SAR处理技术

联合像素干涉SAR处理技术属于第二代时序干涉SAR技术,技术流程分为3步:联合像素信号模型建立,拟合优度检验,空域自适应滤波。本文在一维KSTEST统计检验方法基础上,提出了基于多维联合像素的非参数拟合优度检验方法,利用实测数据将该方法与传统的多时相干涉SAR技术结果对比,使形变监测点空间密度得到有效提升,形变监测能力得到进一步提高。

On the Power Performance of Test Statistics for the Generalized Rayleigh Interval Grouped Data

In this paper, the weighted Kolmogrov-Smirnov, Cramer von-Miss and the Anderson Darling test statistics are considered as goodness of fit tests for the generalized Rayleigh interval grouped data. An extensive simulation process is conducted to evaluate their controlling of type 1 error and their power functions. Generally, the weighted Kolmogrov-Smirnov test statistics show a relatively better performance than both, the Cramer von-Miss and the Anderson Darling test statistics. For large sample values, the Anderson Darling test statistics cannot control type 1 error but for relatively small sample values it indicates a better performance than the Cramer von-Miss test statistics. Best selection of the test statistics and highlights for future studies are also explored.

Nonlinear Fitting Method of Long-Term Distributions for Statistical Analysis of Extreme Negative Surge Elevations

Hydrologic frequency analysis plays an important role in coastal and ocean engineering for structural design and disaster prevention in coastal areas. This paper proposes a Nonlinear Least Squares Method (NLSM), which estimates the three unknown parameters of the Weibull distribution simultaneously by an iteration method. Statistical test shows that the NLSM fits each data sample well. The effects of different parameter-fitting methods, distribution models, and threshold values are also discussed in the statistical analysis of storm set-down elevation. The best-fitting probability distribution is given and the corresponding return values are estimated for engineering design.

A viable method for goodness-of-fit test in maximum likelihood fit

A test statistic is proposed to perform the goodness-of-fit test in the unbinned maximum likelihood fit. Without using a detailed expression of the efficiency function, the test statistic is found to be strongly correlated with the maximum likelihood function if the efficiency function varies smoothly. We point out that the correlation coefficient can be estimated by the Monte Carlo technique. With the established method, two examples are given to illustrate the performance of the test statistic.

THE GOODNESS-OF-FIT TEST BY USING MULTIVARIATE RANDOM WEIGHTING METHOD

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Goodness-of-fit tests for vector autoregressive models in time series

The paper proposes and studies some diagnostic tools for checking the goodness-of-fit of general parametric vector autoregressive models in time series. The resulted tests are asymptotically chi-squared under the null hypothesis and can detect the alternatives converging to the null at a parametric rate. The tests involve weight functions,which provides us with the flexibility to choose scores for enhancing power performance,especially under directional alternatives. When the alternatives are not directional,we construct asymptotically distribution-free maximin tests for a large class of alternatives. A possibility to construct score-based omnibus tests is discussed when the alternative is saturated. The power performance is also investigated. In addition,when the sample size is small,a nonparametric Monte Carlo test approach for dependent data is proposed to improve the performance of the tests. The algorithm is easy to implement. Simulation studies and real applications are carried out for illustration.

GOODNESS-OF-FIT TEST ON TWO SAMPLES

In this paper, a new statistics for testing two samples coming from the same population is derived from a simple linear model with an artificial parameter. Its limit distribution is a chi-squared distribution with 2 degrees of freedom under null hypothesis and the limit distribution is a noncentral chi-squared distribution with 2 degrees of freedom under certain sequence of alternative hypothesis. Finally, we make power comparison with other tests on two samples, especially, with Smirnov statistics.

FDR Control for Pairwise Comparisons of Normal Means Based on Goodness of Fit Test

This study is undertaken to apply a bootstrap method of controlling the false discovery rate （FDR） when performing pairwise comparisons of normal means. Due to the dependency of test statistics in pairwise comparisons, many conventional multiple testing procedures can＇t be employed directly. Some modified pro- cedures that control FDR with dependent test statistics are too conservative. In the paper, by bootstrap and goodness-of-fit methods, we produce independent p-values for pairwise comparisons. Based on these indepen- dent p-values, plenty of procedures can be used, and two typical FDR controlling procedures are applied here. An example is provided to illustrate the proposed approach. Extensive simulations show the satisfactory FDR control and power performance of our approach. In addition, the proposed approach can be easily extended to more than two normal, or non-normal, balance or unbalance cases.

基于剪切波变换和拟合优度检验的遥感图像去噪

针对遥感图像中的高斯白噪声,提出一种基于剪切波变换和拟合优度检验的遥感图像去噪算法.首先,将含噪遥感图像通过剪切波变换多尺度分解得到不同子带,利用剪切波域下高斯白噪声系数的统计关系估计去噪阈值;其次,计算高频子带的拟合优度检验统计量,将统计量与去噪阈值相比较进行去噪;最后,对系数矩阵进行剪切波逆变换重建去噪图像.仿真实验结果表明,该算法能有效去除遥感图像中的高斯噪声,保持图像的边缘纹理信息,并且在不同噪声水平下,均获得了较高的峰值信噪比,其中与剪切波阈值去噪算法相比平均提高0.33 dB.

基于退化量分布的剩余电流动作断路器可靠性评估

剩余电流动作断路器直接影响低压配电系统的安全性和可靠性。为了研究剩余电流动作断路器的可靠性,使用基于退化量分布的建模方法对其性能退化过程进行研究,并预测出其使用寿命。首先,对剩余电流动作断路器的失效机理进行分析,然后设计实施剩余电流动作断路器的加速退化试验,建立了退化量服从正态分布的可靠度分析模型,采用最小二乘法对模型中的参数进行估计,从而对剩余电流动作断路器的可靠性进行评估并绘制可靠度曲线。