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.     

Inference and optimal design of multiple constant-stress testing for generalized half-normal distribution under type-II progressive censoring

The generalized half-normal (GHN) distribution and progressive type-II censoring are considered in this article for studying some statistical inferences of constant-stress accelerated life testing. The EM algorithm is considered to calculate the maximum likelihood estimates. Fisher information matrix is formed depending on the missing information law and it is utilized for structuring the asymptomatic confidence intervals. Further, interval estimation is discussed through bootstrap intervals. The Tierney and Kadane method, importance sampling procedure and Metropolis-Hastings algorithm are utilized to compute Bayesian estimates. Furthermore, predictive estimates for censored data and the related prediction intervals are obtained. We consider three optimality criteria to find out the optimal stress level. A real data set is used to illustrate the importance of GHN distribution as an alternative lifetime model for well-known distributions. Finally, a simulation study is provided with discussion.     

基于随机p-鲁棒优化的国家血液战略储备库选址-库存模型

以往的救灾实践对建立国家血液战略储备体系提出了迫切要求。国家血液战略储备库的建设问题亟待解决。由于血液产品特性以及应急血液保障特性的存在，使得国家血液战略储备库的选址决策具有一定的复杂性。本文将问题定位为选址-库存问题。首先，以应急条件下血液保障及时度最高为目标，构建了一个不确定环境下考虑多情景、多血型、多阶段、带提前期、有容量限制、日常随机需求、有预算约束及协同定位的国家血液战略储备库选址-库存模型。同时，为了规避应急条件下的不确定风险，进一步构建了国家血液战略储备库选址-库存问题的随机p-鲁棒优化模型。该模型为离散非线性混合整数规划模型，难以快速精确求解。故基于模型性质，设计了相应的遗传算法。最后，设计了两组算例验证模型与算法的有效性。其中，第1组算例基于我国大陆地区31个省级血液中心与省级行政区的数据，并根据不同预算值给出6个算例，得到了国家血液战略储备库的选址-库存决策方案。第2组算例为6个不同规模的模拟算例，用来测试不同规模下的算法性能。算例结果表明：遗传算法的性能更好；鲁棒解与确定性模型最优值相差不大（最大差距≤1.08%），可降低不确定性导致的风险。实践中，可对本文所建模型稍作改进，应用于具有类似特征的易腐品（药品、粮食等）应急物资储备库选址-库存决策。     

万物互联时代的“算法”崇拜与资源配置变革

随着万物互联时代的来临，人类资源配置将被各种人工智能“算法”①所取代。万物互联产生的数据将成为社会资源的重要形态，也是支撑人工智能“算法”的重要基础。数据资源的安全、所有权归属、开发利用效率以及保密等重要议题，将成为民族学、经济学、社会学和心理学等相关学科关注和研究的新热点。我国是一个统一的多民族国家，如何将丰富的民族多样化资源转化为支持万物互联资源网络协同优化配置人工智能“算法”的数据，需要从民族历史、文化、生物、信息处理技术等相关学科做深入的理论和实证研究。在万物互联时代，因资源配置方式与配置机制发生变革，经济学一般不再直接求解资源最优配置的策略集，更多是从理论和实证方面研究资源配置中人工智能“算法”的构造原理、构造条件与影响因素。万物互联时代，社会和谐秩序的构建，需要政府建立和维护万物互联网络的安全防护体系，并对资源配置中人工智能技术演变路径作规范和指引。     

航线网络中需求无约束估计的非参数选择模型

传统的航空客运需求无约束估计方法仅针对平行直达航班中的顾客需求“溢出”和“再 现”问题，未能考虑航空网络中直达和中转联程航班之间的网络替代效应． 基于顾客偏好排序列表定义了航空网络顾客类型集合，建立了考虑顾客策略行为的网络型非参数离散选择模型．考虑到网络环境下历史预售数据的不完备性，站在线上和线下交易平台的角度，分别建立了非截尾和截尾需求情况下的完备数据对数似然函数． 采用 EM 算法对顾客到达率和概率质量函数进行联合估计，并提出了网络环境下的顾客“初始需求”、“再现需求”和“溢出需求”无约束估计计算方法．通过数值模拟验证了所提方法的可行性和有效性，相较于现有方法能准确反映产品间网络替代效应对顾客选择行为影响，从而更加有效地避免对历史顾客“初始需求”的高估问题．     

Water cycle algorithm for solving the reliability-redundancy allocation problem with a choice of redundancy strategies

Abstract

This paper proposes a new mathematical model for the reliability-redundancy allocation problem (RRAP) with a choice of redundancy strategies. To maximize the reliability of a system, this model chooses the best redundancy strategy from among both active and standby ones for each subsystem. For those with a standby strategy, a continuous time Markov chain model is used to calculate the exact reliability values. In order to solve the proposed mixed-integer non-linear programing model, a powerful evolutionary algorithm, called water cycle algorithm (WCA), is developed and implemented on three famous benchmark problems. Finally, the results of different benchmark problems are compared with those previously reported to show the superiority of the proposed model and the efficiency of WCA.     

震后伤员救援车辆两阶段规划模型及算法研究

不确定情景下的应急物资配送和车辆路径优化问题是目前国内研究的一大热点，其中 震后伤员运送车辆调度及路径优化问题随着近几年地震等自然灾害的频繁发生也逐渐引起人 们的重视． 本文采用聚合优化算法对灾区进行应急救援区域划分，建立了以总救援时间最短和 相对综合救援权重值最大为目标的救援车辆两阶段数学规划模型，设计了带三角函数变异的 离散型萤火虫优化算法进行求解，并与常规算法求解结果做了对比分析． 通过算例进行模拟实 验表明，所提出的模型和算法可以有效解决地震情景下的伤员救援车辆调度问题，且改进算法 的全局搜索性能更优．     

考虑实时预倒箱的出口箱堆场多场桥调度优化

在出口集装箱堆场的实际作业过程中，倒箱是制约场桥作业效率的瓶颈之一。为提高出口箱堆场的作业效能，减少船舶装船作业时间，采用实时预倒来降低倒箱的影响，研究出口箱堆场多场桥调度优化问题。考虑待提箱作业次序固定、场桥间保持安全距离及不可跨越的现实约束，兼顾内集卡的等待上限，侧重场桥作业过程中的实时预倒箱，构建了以带惩罚因子的内集卡总等待时间最少为目标的混合整数线性规划模型。基于问题自身的特点设计了混合和声模拟退火算法，得出了各场桥的行走路径与实时预倒箱方案。在算例实验中，通过与不考虑实时预倒箱的方案、FCFS方案以及下界进行对比，验证了考虑实时预倒箱的场桥调度模型及算法的有效性，为集装箱码头出口箱堆场的场桥调度提供参考。     

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。     

Random effects regression mixtures for analyzing infant habituation

Random effects regression mixture models are a way to classify longitudinal data (or trajectories) having possibly varying lengths. The mixture structure of the traditional random effects regression mixture model arises through the distribution of the random regression coefficients, which is assumed to be a mixture of multivariate normals. An extension of this standard model is presented that accounts for various levels of heterogeneity among the trajectories, depending on their assumed error structure. A standard likelihood ratio test is presented for testing this error structure assumption. Full details of an expectation-conditional maximization algorithm for maximum likelihood estimation are also presented. This model is used to analyze data from an infant habituation experiment, where it is desirable to assess whether infants comprise different populations in terms of their habituation time.