A multivariate logit model with marginal canonical association

In this work, a generalization of the Goodman Association Model to the case of q, q > 2, categorical variables which is based on the idea of marginal modelling discussed by Gloneck–McCullagh is introduced; the difference between the proposed generalization and two models, previously introduced by Becker and Colombi, is discussed. The Becker generalization is not a marginal model because it does not imply Logit Models for the marginal probabilities, and because it is based on the conditional approach of modelling the association. The Colombi model is only partially a marginal model because it uses simple logit models for the univariate marginal probabilities but is based on the conditional approach of modelling the association. It is also shown that the maximum likelihood estimation of the parameters of the new model is feasible and, to compute the maximum likelihood estimates, an algorithm is proposed, which is a numerically convenient compromise between the constrained optimization approach of Lang and the straightforward use of the Fisher Scoring Algorithm suggested by Glonek–McCullagh.Finally, the proposed model is used to analyze a data set concerning work accidents which occurred to workers at some Italian firms during the years 1994–1996.     

Shape Constrained Non‐parametric Estimators of the Baseline Distribution in Cox Proportional Hazards Model

Abstract. We investigate non‐parametric estimation of a monotone baseline hazard and a decreasing baseline density within the Cox model. Two estimators of a non‐decreasing baseline hazard function are proposed. We derive the non‐parametric maximum likelihood estimator and consider a Grenander type estimator, defined as the left‐hand slope of the greatest convex minorant of the Breslow estimator. We demonstrate that the two estimators are strongly consistent and asymptotically equivalent and derive their common limit distribution at a fixed point. Both estimators of a non‐increasing baseline hazard and their asymptotic properties are obtained in a similar manner. Furthermore, we introduce a Grenander type estimator for a non‐increasing baseline density, defined as the left‐hand slope of the least concave majorant of an estimator of the baseline cumulative distribution function, derived from the Breslow estimator. We show that this estimator is strongly consistent and derive its asymptotic distribution at a fixed point.     

Distribution of the Activity Time in Network Analysis: A Critical Revisit with a Gamma Alternative

Network analysis is an important technique for project management. However, the current literature is biased toward beta distribution for modeling activity times. In this article, we have critically examined the role of beta distribution. It has been shown that beta distribution plays no specific role. Further, the method for calculating the chance of project completion must be revised. We have accordingly suggested an alternative modeling in terms of gamma distribution and proposed how to calculate the chance of project completion by taking into consideration both critical and noncritical paths. For demonstration purposes, we have considered a small project.     

网络时代对高校思想政治工作的挑战

网络已经走进了大学生的生活。网络以其虚拟性、时效性、开放性、娱乐性、互动性等技术特点带来巨大的社会效能的同时 ,也给高校思想政治工作带来严峻的挑战和难得的机遇。面对这一挑战和机遇 ,高校思想政治工作者应充分利用网络优势 ,开发新的思想政治工作资源 ,实现思想政治工作方式科学化、现代化 ,使思想政治工作永葆青春活力。     

一类新的自适应非单调谱投影梯度法

给出了求解凸约束优化的一类新的自适应非单调谱投影梯度法.通过引入具有自适应性的权重参数,使算法在迭代过程中能自动调节非单调策略. 在适当条件下证明了算法的收敛性.数值试验结果表明,该算法在一定程度上能减少在线搜索过程中对非单调参数M的依赖.     

约束平面选址问题的蚂蚁算法

对带有区域限制的平面选址问题，给出一种基于人工蚂蚁优化思想的新的求解方法。经数值计算、验证和比较，得到了满意的效果。     

Mechanisms for Increasing Sourcing from Capacity‐Constrained Local Suppliers

The fresh produce supply chain is characterized by large (mainstream) farms that are located far from consumers, and capacity‐constrained (local) farms that are located close to the consumer. In this setting, we study: (i) how leadtime and capacity asymmetry between mainstream and local farms affect a retail grocer's order policy for fresh produce, and (ii) how various operational mechanisms can increase the amount sourced from local farms. We show that this supply chain structure is disadvantageous for local suppliers (farms) because it induces the retailer to treat the local supply as a de facto responsive supply without paying a premium for the responsiveness. This disadvantage is exacerbated when the retailer's objective is to achieve a high service level. We study three mechanisms that can improve conditions for local farms: working with an intermediary, backhauling, and a retail order policy, purchase guarantee, that explicitly supports local farms. The intermediary and backhauling mechanisms help the local farm by making local supply more attractive to the retailer, inducing her to order more locally sourced produce. The intermediary reduces the retailer's overstock and stockout costs whereas backhauling increases the average margin. The purchase guarantee order policy helps local farms at the expense of retail profit. However, we show that purchase guarantee and backhauling are complementary mechanisms that together can benefit the retailer and local farms.     

Natural cubic splines for the analysis of Alzheimer's clinical trials

Mixed model repeated measures (MMRM) is the most common analysis approach used in clinical trials for Alzheimer's disease and other progressive diseases measured with continuous outcomes over time. The model treats time as a categorical variable, which allows an unconstrained estimate of the mean for each study visit in each randomized group. Categorizing time in this way can be problematic when assessments occur off-schedule, as including off-schedule visits can induce bias, and excluding them ignores valuable information and violates the intention to treat principle. This problem has been exacerbated by clinical trial visits which have been delayed due to the COVID19 pandemic. As an alternative to MMRM, we propose a constrained longitudinal data analysis with natural cubic splines that treats time as continuous and uses test version effects to model the mean over time. Compared to categorical-time models like MMRM and models that assume a proportional treatment effect, the spline model is shown to be more parsimonious and precise in real clinical trial datasets, and has better power and Type I error in a variety of simulation scenarios.