KERNEL DENSITY ESTIMATION WITH MISSING DATA AND AUXILIARY VARIABLES

In most parametric statistical analyses, knowledge of the distribution of the response variable, or of the errors, is important. As this distribution is not typically known with certainty, one might initially construct a histogram or estimate the density of the variable of interest to gain insight regarding the distribution and its characteristics. However, when the response variable is incomplete, a histogram will only provide a representation of the distribution of the observed data. In the AIDS Clinical Trial Study protocol 175, interest lies in the difference in CD4 counts from baseline to final follow-up, but CD4 counts collected at final follow-up were incomplete. A method is therefore proposed for estimating the density of an incomplete response variable when auxiliary data are available. The proposed estimator is based on the Horvitz–Thompson estimator, and the propensity scores are estimated nonparametrically. Simulation studies indicate that the proposed estimator performs well.     

Relational collaboration among spatial multipoint competitors

The presence of network ties between multipoint competitors is frequently assumed but rarely examined directly. The outcomes of multipoint competition, therefore, are better understood than their underlying relational mechanisms. Using original fieldwork and data that we have collected on an interorganizational network of patient transfer relations within a regional community of hospitals, we report and interpret estimates of Exponential Random Graph Models (ERGM) that specify the probability of observing network ties between organizations as a function of the degree of their spatial multipoint contact. We find that hospitals competing more intensely for patients across multiple geographical segments of their market (spatial multipoint competitors) are significantly more likely to collaborate. This conclusion is robust to alternative explanations for the formation of network ties based on organizational size differences, resource complementarities, performance differentials, and capacity constraints. We show that interorganizational networks between spatial multipoint competitors are characterized by clear tendencies toward clustering and a global core-periphery structure arising as consequences of multiple mechanisms of triadic closure operating simultaneously. We conclude that the effects of competition on the structure of interorganizational fields depends on how markets as physical and social settings are connected by cross-cutting network ties between competitors.     

Multiple imputation for missing data in a longitudinal cohort study: a tutorial based on a detailed case study involving imputation of missing outcome data

Multiple imputation (MI), a two-stage process whereby missing data are imputed multiple times and the resulting estimates of the parameter(s) of interest are combined across the completed datasets, is becoming increasingly popular for handling missing data. However, MI can result in biased inference if not carried out appropriately or if the underlying assumptions are not justifiable. Despite this, there remains a scarcity of guidelines for carrying out MI. In this paper we provide a tutorial on the main issues involved in employing MI, as well as highlighting some common pitfalls and misconceptions, and areas requiring further development. When contemplating using MI we must first consider whether it is likely to offer gains (reduced bias or increased precision) over alternative methods of analysis. Once it has been decided to use MI, there are a number of decisions that must be made during the imputation process; we discuss the extent to which these decisions can be guided by the current literature. Finally we highlight the importance of checking the fit of the imputation model. This process is illustrated using a case study in which we impute missing outcome data in a five-wave longitudinal study that compared extremely preterm individuals with term-born controls.     

由m序列构造的同级类m序列及性能研究

根据实践中用状态剪接法获得的一种级数有限的类m序列,用数学手段证明级数为任意数时这种类m序列仍然存在,方法是将m序列移位寄存器的状态和有效反馈位都按奇数位和偶数位分开,分别研究它们对反馈值的影响,从理论上证明了级数为任意整数时,相关的共轭状态都符合类m序列的形成条件。经过分析和测试表明,该类m序列具有良好的伪随机特性。     

瓶颈约束理论下城建公司的生产物流调度优化研究

依据瓶颈约束理论对企业生产物流的调度和规划等因素的影响,以城建公司的生产制造为例进行了一个间断式生产物流的漠拟仿真研究和评价。在对调研数据进行分析、整理的基础上,找到制约企业物流效率提高的瓶颈;通过建立生产物流系统的调度模型,制定调度方法,提出了改善措施和调度方案。运用人工随机模拟仿真方法,验证改善方案给城建公司昕带来的产量改进效果,结果表明,产量有显菩提高。     

福建省不同景观资源分布特征的比较分析

采用最近邻点距离法,对福建省省级以上风景名胜区、自然保护区及森林公园空间分布特征进行研究,同时对3种景观资源的景源现状及特征进行了比较。研究表明,福建省总体景观资源空间分布为显著的聚集分布,其中风景名胜区、森林公园及自然保护区等3种景观资源均为随机分布,3种景观资源的景源特征差异比较大,建议在景观较稀疏的区域增加新的景区,通过各种廊道连接构成全省景观网络系统,以保证全省景观资源系统的健康有序发展。     

随机需求直接发运的运输与库存整合优化研究

分销系统的运输与库存整合优化是供应链管理的重要研究课题.本文分析了随机需求两级分销系统的三种配送策略,并具体给出了基于整车直接发运策略的运输决策与库存控制整合优化数学模型,可得到最优的两级最优库存水平和最优的运输频率.最后对给出的算例,利用MATLAB7的规划求解功能容易获得了问题的最优解.     

不确定需求环境下多级库存系统优化与协调模型研究

论文首先将模糊随机变量扩展为混合随机变量,并用来描述不确定需求.然后对多级单周期库存系统在模糊随机和混合随机需求环境下的优化与协调问题作了研究,提出了通过集成库存管理使库存系统整体收益期望值最大化的优化模型和协调合作伙伴利益的数量折扣契约模型,并根据遗传算法理论和计算机模拟技术设计了求解模型的智能算法.最后通过实例对模型进行了仿真分析.     

An application of the mixed-effects model and pattern mixture model to treatment groups with differential missingness suspected not-missing-at-random

Likelihood-based, mixed-effects models for repeated measures (MMRMs) are occasionally used in primary analyses for group comparisons of incomplete continuous longitudinal data. Although MMRM analysis is generally valid under missing-at-random assumptions, it is invalid under not-missing-at-random (NMAR) assumptions. We consider the possibility of bias of estimated treatment effect using standard MMRM analysis in a motivational case, and propose simple and easily implementable pattern mixture models within the framework of mixed-effects modeling, to handle the NMAR data with differential missingness between treatment groups. The proposed models are a new form of pattern mixture model that employ a categorical time variable when modeling the outcome and a continuous time variable when modeling the missingness-data patterns. The models can directly provide an overall estimate of the treatment effect of interest using the average of the distribution of the missingness indicator and a categorical time variable in the same manner as MMRM analysis. Our simulation results indicate that the bias of the treatment effect for MMRM analysis was considerably larger than that for the pattern mixture model analysis under NMAR assumptions. In the case study, it would be dangerous to interpret only the results of the MMRM analysis, and the proposed pattern mixture model would be useful as a sensitivity analysis for treatment effect evaluation.