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.     

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.     

Inference on a progressive type I interval-censored truncated normal distribution

In this paper, we consider the problem of making statistical inference for a truncated normal distribution under progressive type I interval censoring. We obtain maximum likelihood estimators of unknown parameters using the expectation-maximization algorithm and in sequel, we also compute corresponding midpoint estimates of parameters. Estimation based on the probability plot method is also considered. Asymptotic confidence intervals of unknown parameters are constructed based on the observed Fisher information matrix. We obtain Bayes estimators of parameters with respect to informative and non-informative prior distributions under squared error and linex loss functions. We compute these estimates using the importance sampling procedure. The highest posterior density intervals of unknown parameters are constructed as well. We present a Monte Carlo simulation study to compare the performance of proposed point and interval estimators. Analysis of a real data set is also performed for illustration purposes. Finally, inspection times and optimal censoring plans based on the expected Fisher information matrix are discussed.     

随机模糊折现现金流融资决策模型建立及求解

企业未来投资收益既具有随机性又表现出一定的模糊性,而将未来单位资本投资收益率定义为随机模糊变量,按照企业发展的特点将其分为低、中、高三个阶段,并在随机模糊环境下建立了融资决策模型,最后利用基于随机模糊模拟的SPSA算法对模型进行求解,结合算例对算法的有效性进行了解释。     

具有模糊旅行时间的VRP的一种混合遗传算法

传统确定性车辆路径问题是近几十年来运筹学领域研究的一个热点问题.但在许多实际的应用中,由于受客观世界中存在的不确定性因素以及人类观察、认识事物的模糊性的影响,车辆路径问题的某些参数可能是模糊的、不确定的.文中传统确定性车辆路径问题被扩展为具有模糊特征的模糊车辆路径问题.在对具有模糊旅行时间的车辆路径问题进行简单描述的基础上,构建了该问题的数学模型,并通过将模糊逻辑、模糊控制方法与传统车辆路径问题的遗传算法进行有效结合,提出了解决该问题的一种混合遗传算法.最后给出了该问题的一个计算实例,并通过随机模拟试验验证了该算法的有效性和优越性.     

Approximate composite marginal likelihood inference in spatial generalized linear mixed models

Non-Gaussian spatial responses are usually modeled using spatial generalized linear mixed model with spatial random effects. The likelihood function of this model cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. There are numerical ways to maximize the likelihood function, such as Monte Carlo Expectation Maximization and Quadrature Pairwise Expectation Maximization algorithms. They can be applied but may in such cases be computationally very slow or even prohibitive. Gauss–Hermite quadrature approximation only suitable for low-dimensional latent variables and its accuracy depends on the number of quadrature points. Here, we propose a new approximate pairwise maximum likelihood method to the inference of the spatial generalized linear mixed model. This approximate method is fast and deterministic, using no sampling-based strategies. The performance of the proposed method is illustrated through two simulation examples and practical aspects are investigated through a case study on a rainfall data set.     

Cox regression of clustered event times with covariates missing not at random

Motivated by a recent tuberculosis (TB) study, this paper is concerned with covariates missing not at random (MNAR) and models the potential intracluster correlation by a frailty. We consider the regression analysis of right‐censored event times from clustered subjects under a Cox proportional hazards frailty model and present the semiparametric maximum likelihood estimator (SPMLE) of the model parameters. An easy‐to‐implement pseudo‐SPMLE is then proposed to accommodate more realistic situations using readily available supplementary information on the missing covariates. Algorithms are provided to compute the estimators and their consistent variance estimators. We demonstrate that both the SPMLE and the pseudo‐SPMLE are consistent and asymptotically normal by the arguments based on the theory of modern empirical processes. The proposed approach is examined numerically via simulation and illustrated with an analysis of the motivating TB study data.     

远海岛礁冷链物流体系优化模型及其集成算法

为研究远海岛礁渔获冷链物流的运行方案,本文选择了岛礁冷库储存中转和海上直接收购等两种不同的渔获运输方式,同时考虑了中转冷库选址、中转运输和直接收购船舶配备、航线配置、在渔场停留时间和渔获运输损耗等诸多复杂问题,构建了远海冷链物流体系组合优化模型。求解时,提出了一种以遗传算法（GA）为外部框架,以改进的模拟植物生长算法（PGSA）为内部模块,通过信息传递实现内外连接交互的集成算法。最后,以我国南海远海岛礁及渔场为实例进行分析,取得了较好效果。通过与传统遗传算法对比,本文优化结果、稳定性及计算速度等均有明显提高,从而证明了本文所建模型与算法的合理性和有效性。     

被遗忘权的制度缺失、发展困境与中国构建路径

数据时代面临的个人信息受到侵害、个人隐私泄露、算法歧视等一系列重大问题,应将以保护信息主体权利为目的的被遗忘权确立下来。欧美被遗忘权在隐私与自由之间存在的冲突背后所体现的是被遗忘权对于各国需求的重要性,我国现有法律制度中规定的删除权与欧盟所确立的被遗忘权是不能等同的。现行被遗忘权面临着调整对象的设定缺陷、权利内容的设定偏差、责任主体的设定不足三方面的发展困境。学者热议与实践需求在一定程度上表明我国应当引入被遗忘权,并应从调整对象的优化构建、权利内容的改造构建、责任承担的明确构建搭建起中国的路径选择,以解决网络信息社会的发展带给我们的新挑战。