A comparison of regression models for ordinal data in an analysis of transplanted-kidney function

Three regression models for ordinal data, those of Fienberg, McCullagh, and Anderson, are applied to an analysis of kidney function among transplant recipients. The conclusions arising from each model are presented and contrasted.     

关于连续生产系统价值工程问题的研究

本文针对流程工业连续生产型企业推广价值工程时遇到的困难,对这类系统产品的功能评价、价值评价等问题进行了探索,给出了连续生产系统二类价值工程问题的数学描述,并对几个实际问题进行应用尝试,取得了成功。     

传统工科专业人才培养改革的探索与实践——以重庆文理学院机械工程专业为例

新时代对我国高等工程教育提出了新的要求,工程教育应加强改革创新,着力解决工程技术人才供需结构失衡,专业建设及人才培养对接新经济、新产业不够,学生新知识、新技术、新素养不足等难题。为支撑和引领地方经济发展,重庆文理学院主动对接本区域智能制造相关新兴产业,积极探索传统工科专业转型发展的模式与路径,按照新工科的特征、内涵和要求转型发展机械工程专业,确立专业人才培养新理念,将行业和企业新知识、新技术、新标准等融入人才培养过程中,构建了学科交叉融合的工业机器人新工科工程技术人才培养新体系,重点培养能够适应当前及未来变化需要的工业机器人相关工程技术人才,取得了较好的实效。     

A class of bivariate regression models for discrete and/or continuous responses

ABSTRACT

A general class of models for discrete and/or continuous responses is proposed in which joint distributions are constructed via the conditional approach. It is assumed that the distributions of one response and of the other response given the first one belong to exponential family of distributions. Furthermore, the marginal means are related to the covariates by link functions and a dependency structure between the responses is inserted into the model. Estimation methods, diagnostic analysis and a simulation study considering a Bernoulli-exponential model, a particular case of the class, are presented. Finally, this model is used in a real data set.     

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.     

ECM-IS视角下移动政务APP用户持续使用意愿影响因素研究

随着“互联网+”时代的到来，移动政务APP快速发展，但也存在功能单一、满意度不高、用户持续使用意愿较低的问题，要促使其健康发展，必须重视用户的使用体验和使用意愿。本研究以ECM-IS模型理论为基础，构建了移动政务APP用户持续使用意愿影响因素模型，据此设计调查问卷并进行实证研究。研究结果显示，移动政务APP的系统质量是满足用户期望的重要因素，而用户感知对于提升用户的满意度以及促使持续使用具有重要的意义，其中，感知便利性和感知有用性的积极作用尤为明显。最后，从促使用户持续使用的角度，提出了完善移动政务APP系统功能、突出实用优势，兼顾趣味功能，以及重视用户感知、强调人文关怀等对策和建议。     

Another Remark on the Alternative Expectation Formula

Students in a calculus-based probability course will often see the expectation formula for nonnegative continuous random variables in terms of the survival function. This alternative expectation formula has a wide spectrum of applications. It is natural to ask whether there is a multivariate version of this formula. This note gives an affirmative answer by establishing such a formula using two different approaches. The two approaches employed in this note correspond to the two approaches for the univariate case. Supplementary materials for this article are available online.     

A stochastically curtailed two-arm randomised phase II trial design for binary outcomes

Randomised controlled trials are considered the gold standard in trial design. However, phase II oncology trials with a binary outcome are often single-arm. Although a number of reasons exist for choosing a single-arm trial, the primary reason is that single-arm designs require fewer participants than their randomised equivalents. Therefore, the development of novel methodology that makes randomised designs more efficient is of value to the trials community. This article introduces a randomised two-arm binary outcome trial design that includes stochastic curtailment (SC), allowing for the possibility of stopping a trial before the final conclusions are known with certainty. In addition to SC, the proposed design involves the use of a randomised block design, which allows investigators to control the number of interim analyses. This approach is compared with existing designs that also use early stopping, through the use of a loss function comprised of a weighted sum of design characteristics. Comparisons are also made using an example from a real trial. The comparisons show that for many possible loss functions, the proposed design is superior to existing designs. Further, the proposed design may be more practical, by allowing a flexible number of interim analyses. One existing design produces superior design realisations when the anticipated response rate is low. However, when using this design, the probability of rejecting the null hypothesis is sensitive to misspecification of the null response rate. Therefore, when considering randomised designs in phase II, we recommend the proposed approach be preferred over other sequential designs.     

A dimensionality reduction method of continuous dependent variables based supervised Laplacian eigenmaps

Dimensionality reduction is one of the important preprocessing steps in high-dimensional data analysis. In this paper we propose a supervised manifold learning method, it makes use of the information of continuous dependent variables to distinguish intrinsic neighbourhood and extrinsic neighbourhood of data samples, and construct two graphs according to these two kinds of neighbourhoods. Following the idea of Laplacian eigenmaps, we reveal that on the low-dimensional manifold the neighbourhood structure can be preserved or even improved. Our approach has two important characteristics: (i) it uses dependent variables to find an informative low-dimensional projection which is robust to noisy independent variables and (ii) the objective function simultaneously enlarges the distance between dissimilar samples and pushes similar samples close to each other according to the graph constructed with the help of continuous dependent variables. Our experiments demonstrate that the effectiveness of our method is over their traditional rivals.