加权改进模糊相似选择方法及其应用

本文对改进模糊相似选择方法作出了进一步的改进,综合权重和海明距离两方面因素,对事物按优劣程度排序,从而更加准确有效,符合实际。     

一类n阶迭代泛函微分方程解的存在唯一性

利用Banach不动点定理讨论一类带有初值条件的n阶迭代泛函微分方程解的存在性和唯一性.     

Empirical likelihood confidence intervals under imputation for missing survey data from stratified simple random sampling

Missing observations due to non‐response are commonly encountered in data collected from sample surveys. The focus of this article is on item non‐response which is often handled by filling in (or imputing) missing values using the observed responses (donors). Random imputation (single or fractional) is used within homogeneous imputation classes that are formed on the basis of categorical auxiliary variables observed on all the sampled units. A uniform response rate within classes is assumed, but that rate is allowed to vary across classes. We construct confidence intervals (CIs) for a population parameter that is defined as the solution to a smooth estimating equation with data collected using stratified simple random sampling. The imputation classes are assumed to be formed across strata. Fractional imputation with a fixed number of random draws is used to obtain an imputed estimating function. An empirical likelihood inference method under the fractional imputation is proposed and its asymptotic properties are derived. Two asymptotically correct bootstrap methods are developed for constructing the desired CIs. In a simulation study, the proposed bootstrap methods are shown to outperform traditional bootstrap methods and some non‐bootstrap competitors under various simulation settings. The Canadian Journal of Statistics 47: 281–301; 2019 © 2019 Statistical Society of Canada     

The Impact of Portfolio Location Uncertainty on Probabilistic Seismic Risk Analysis

Probabilistic seismic risk analysis is a well‐established method in the insurance industry for modeling portfolio losses from earthquake events. In this context, precise exposure locations are often unknown. However, so far, location uncertainty has not been in the focus of a large amount of research. In this article, we propose a novel framework for treatment of location uncertainty. As a case study, a large number of synthetic portfolios resembling typical real‐world cases were created. We investigate the effect of portfolio characteristics such as value distribution, portfolio size, or proportion of risk items with unknown coordinates on the variability of loss frequency estimations. The results indicate that due to loss aggregation effects and spatial hazard variability, location uncertainty in isolation and in conjunction with ground motion uncertainty can induce significant variability to probabilistic loss results, especially for portfolios with a small number of risks. After quantifying its effect, we conclude that location uncertainty should not be neglected when assessing probabilistic seismic risk, but should be treated stochastically and the resulting variability should be visualized and interpreted carefully.     

Small orthogonal main effect plans with four factors

In this paper we study orthogonal main effect plans with four factors, A table of such designs, where each factor has at most 10 levels, and there are at most 40 runs, is generated. We determine the spectrum of the degrees of freedom of pure error for these designs.     

中国高等教育投入产出效率的综合评价——基于Window-Malmquist指数法

"双一流"建设背景下，基于合作竞争的资源配置模式决定了中国必须提高高等教育投入产出效率。综合运用Window-Malmquist指数法和空间聚类方法测算分析中国31省（市）2004-2015年的高等教育投入产出效率及其演变规律和空间差异，结果显示：（1）总体而言，中国高等教育投入产出效率呈现DEA有效状态，技术进步是其提升的主要贡献因素，但高等教育"区域鸿沟"的存在却导致追赶效应拉低了效率值；（2）从时间演变规律看，中国高等教育投入产出效率相对稳定，但上升趋势并不明显，且存在两极分化或多极分化的可能性；（3）从空间差异情况看，31个省（市）中，江苏高等教育投入产出效率最高，东部沿海地区的高等教育投入产出效率具有"高高"特征，且效率值明显高于其他地区；（4）从空间集聚特征看，在技术进步的作用下不同区域的高等教育投入产出效率存在正向的空间关系，但追赶效应却降低了这种空间联系。     

Quantile regression methods for left-truncated and right-censored data

Left-truncated and right-censored (LTRC) data are encountered frequently due to a prevalent cohort sampling in follow-up studies. Because of the skewness of the distribution of survival time, quantile regression is a useful alternative to the Cox's proportional hazards model and the accelerated failure time model for survival analysis. In this paper, we apply the quantile regression model to LTRC data and develops an unbiased estimating equation for regression coefficients. The proposed estimation methods use the inverse probabilities of truncation and censoring weighting technique. The resulting estimator is uniformly consistent and asymptotically normal. The finite-sample performance of the proposed estimation methods is also evaluated using extensive simulation studies. Finally, analysis of real data is presented to illustrate our proposed estimation methods.     

Generalized linear spatial models in epidemiology: A case study of zoonotic cutaneous leishmaniasis in Tunisia

Generalized linear spatial models (GLSM) are used here to study spatial characters of zoonotic cutaneous leishmaniasis (ZCL) in Tunisia. The response variable stands for the number of affected by district during the period 2001–2002. The model covariates are: climates (temperature and rainfall), humidity and surrounding vegetation status. As the environmental and weather data are not available for all the studied districts, Kriging based on linear interpolation was used to estimate the missing data. To account for unexplained spatial variation in the model, we include a stationary Gaussian process S with a powered exponential spatial correlation function. Moran coefficient, DIC criterion and residuals variograms are used to show the high goodness-of-fit of the GLSM. When compared with the statistical tools used in the previous ZCL studies, the optimal GLSM found here yields a better assessment of the impact of the risk factors, a better prediction of ZCL evolution and a better comprehension of the disease transmission. The statistical results show the progressive increase in the number of affected in zones with high temperature, low rainfall and high surrounding vegetation index. Relative humidity does not seem to affect the distribution of the disease in Tunisia. The results of the statistical analyses stress the important risk of misleading epidemiological conclusions when non-spatial models are used to analyse spatially structured data.     

3D-3C地震资料处理方法在SLG气区的应用

利用转换波（P-SV)的传播速度比纵波低，传播不受孔隙流体的影响，传播规律和纵波有较大的区别，可以进行有效的油气识别。针对鄂尔多斯盆地储层以陆相中的三角洲平原分流河道砂体为主，储层分布变化大，横向上低孔低渗，储层物性的非均质性比较强，P-SV波资料处理与P波资料处理有一些差异的特征,对SLG气区实际3D-3C地震资料处理中的转换波静校正问题、共转换点（CCP）道集抽取方法、转换波的速度分析方法、动校正方法及转换波和纵波比例压缩方法等进行了研究，建立了3D-3C地震资料处理方法和技术，并取得了良好的应用效果，可以将该方法在实际生产中推广应用。