Comparison of causal effect estimators under exposure misclassification

Over the past decades, various principles for causal effect estimation have been proposed, all differing in terms of how they adjust for measured confounders: either via traditional regression adjustment, by adjusting for the expected exposure given those confounders (e.g., the propensity score), or by inversely weighting each subject's data by the likelihood of the observed exposure, given those confounders. When the exposure is measured with error, this raises the question whether these different estimation strategies might be differently affected and whether one of them is to be preferred for that reason. In this article, we investigate this by comparing inverse probability of treatment weighted (IPTW) estimators and doubly robust estimators for the exposure effect in linear marginal structural mean models (MSM) with G-estimators, propensity score (PS) adjusted estimators and ordinary least squares (OLS) estimators for the exposure effect in linear regression models. We find analytically that these estimators are equally affected when exposure misclassification is independent of the confounders, but not otherwise. Simulation studies reveal similar results for time-varying exposures and when the model of interest includes a logistic link.     

Hypothesis Testing of Hazard Ratio Parameters in Marginal Models for Multivariate Failure Time Data

Marginal hazard models for multivariate failure time data have been studied extensively in recent literature. However, standard hypothesis test statistics based on the likelihood method are not exactly appropriate for this kind of model. In this paper, extensions of the three commonly used likelihood hypothesis test statistics are discussed. Generalized Wald, generalized score and generalized likelihood ratio tests for hazard ratio parameters in a marginal hazard model for multivariate failure time data are proposed and their asymptotic distributions examined. The finite sample properties of these statistics are studied through simulations. The proposed method is applied to data from Busselton Population Health Surveys.     

ODE法在多目标规划中的应用

在一定条件下,从多目标规划问题的任一可行解的某邻域出发,建立了沿着所建立的常微分方程组的轨线,关于部分交元总收敛到原多目标规划问题的(弱)有效解。     

The wireless network jamming problem

In adversarial environments, disabling the communication capabilities of the enemy is a high priority. We introduce the problem of determining the optimal number and locations for a set of jamming devices in order to neutralize a wireless communication network. This problem is known as the wireless network jamming problem. We develop several mathematical programming formulations based on covering the communication nodes and limiting the connectivity index of the nodes. Two case studies are presented comparing the formulations with the addition of various percentile constraints. Finally, directions of further research are addressed.     

软预算约束与国企改革：从要素投入的视角分析

论文运用动态模型对软预算约束的形成路径进行了理论研究,并对中国国有企业改革的路径选择进行实证研究。研究结果表明软预算约束起源于企业要素投入与生产模式的匹配模式,而中国国有企业改革路径选择的结果是使要素投入和生产模式满足弱匹配条件。     

供应链中具有产能限制的竞争企业中的信息共享研究

本文研究由一个供货商和两个制造商组成的二级供应链中，制造商之间的横向信息共享策略和供应商的定价问题。在模型中，上游供应商同时为下游两家制造商提供价格相同的原材料，下游制造商生产具有替代性的商品进行数量竞争，并受到相同的产能限制。以Cournot博弈为研究手段，求解了制造商的均衡订货决策和信息共享策略，分析比较了在不同的信息共享策略下制造商的利润、供应商的利润和批发价格。本文在研究制造商的信息共享策略时考虑了上游供应商的批发价格的影响。研究表明，当均衡解受到产能约束时，制造商的信息共享策略会反向。在某些情况下，完全信息共享和完全信息不共享都可能成为博弈的占优策略。上游供应商通过调整批发价格可以影响制造商的信息共享决策。该模型为上游供应商提供了一种最优定价策略，也为下游制造商提供了求解自身最优订货量和信息共享决策的方法。     

Performance of TOC based finite capacity material requirement planning system for a multi-stage assembly factory

This paper aims to develop a finite capacity material requirement planning (FCMRP) system based on TOC philosophy (TOC-MRP) for multi-stage assembly factory that has some bottleneck stations. The proposed TOC-MRP system tries to load and schedule operations on bottleneck stations in a manner that they are free of idle time and overtime. The schedules on non-bottleneck stations will be arranged until they are not conflicting with those on the bottleneck stations. The non-bottleneck stations are allowed to have idle time and overtime if necessary. To analyse whether TOC-MRP is effective, it is compared with a FCMRP method that does not adopt TOC philosophy. The experimental results reveal that the TOC-MRP outperforms the FCMRP without TOC philosophy.     

There's more to the picture than meets the eye: Measuring tangible and intangible capital in two marginal communities in rural Denmark

A missing link in economics has been what Veblen in 1908 termed intangible capital. This includes common norms, trust and high levels of cooperative performance. Intangibles are invisible to the eye and not easily measured in quantitative terms. They nevertheless involve visible, socioeconomic outcomes and should therefore rightly be seen as productive, like tangibles. Thus, uneven levels of intangible capital would explain Differential Economic Performance (DEP) between, say, two firms containing exactly the same stock of physical, economic and human capital. Despite this common sense observation, most economists have failed to see that ‘there's more to the picture than meets the eye’, as Neil Young once sang. We use statistical, historical and fieldwork data from two Danish, marginal rural communities both rich on intangible capital. This to show how intangible capital in the form of social, organisational and cultural capital is accumulated and utilised in situ, at the microlevel. We suggest that the difference between these two, very similar communities should be explained in their varying ability to utilise local stocks of tangible and intangible capital. Drawing on seminal ideas from Bourdieu [The forms of capital. In: Richardson, J.G. (Ed.) Handbook of Theory and Research for the Sociology of Education. Greenwood Press, New York, Westport, CT and London, 1986, pp. 241–58] and the DORA project [Bryden, Differential economic performance in rural areas. In: International Conference on Rural Communities and Identities in the Global Millennium. Malpasino University College, Nainamo, BC, Canada, 2000], we want to develop a ‘total capital’ assessment tool for mapping and measuring socioeconomic development in marginal rural communities. In this way, we hope to count in ‘all’ capital as Schultz [Investment in human capital. In: Kiker, B.F. (Ed.) Investment in Human Capital. Columbia, 1971, pp. 3–21] prophesised. This in order to explain what we term Differential Local Development (DLD), where ‘good’, sustainable development is associated with high economic performance and increase in population.     

碳排放约束与农业全要素生产率增长及分解

针对传统ML指数不具有传递性特征并且面临潜在线性规划无解的问题,将Global Malmquist－Luenberger生产率指数引入到农业全要素生产率的分析框架中,并利用中国31个省市1993—2013年的面板数据测算了碳排放约束条件下农业全要素生产率的增长及其分解。研究表明：是否考虑农业碳排放因素会对农业全要素生产率的测算产生较大影响,忽略碳排放约束会高估区域农业全要素生产率,测算结果可能导致政策偏误;在碳排放约束条件下,中国农业发展普遍存在效率恶化与技术进步并存的局面,农业全要素生产率增长主要由技术进步贡献,农业技术效率不增反减;中国农业全要素生产率增长存在显著的区域差异,东部地区要明显优于其他地区;中国农业大省在碳排放约束条件下未能表现出特别明显的农业优势,农业大省并非农业强省。     

Bayesian Variable Selection Under Collinearity

In this article, we highlight some interesting facts about Bayesian variable selection methods for linear regression models in settings where the design matrix exhibits strong collinearity. We first demonstrate via real data analysis and simulation studies that summaries of the posterior distribution based on marginal and joint distributions may give conflicting results for assessing the importance of strongly correlated covariates. The natural question is which one should be used in practice. The simulation studies suggest that posterior inclusion probabilities and Bayes factors that evaluate the importance of correlated covariates jointly are more appropriate, and some priors may be more adversely affected in such a setting. To obtain a better understanding behind the phenomenon, we study some toy examples with Zellner’s g-prior. The results show that strong collinearity may lead to a multimodal posterior distribution over models, in which joint summaries are more appropriate than marginal summaries. Thus, we recommend a routine examination of the correlation matrix and calculation of the joint inclusion probabilities for correlated covariates, in addition to marginal inclusion probabilities, for assessing the importance of covariates in Bayesian variable selection.