Analysis of mixed outcomes: misclassified binary responses and measurement error in covariates

This paper considers regression models for mixed binary and continuous outcomes, when the true predictor is measured with error and the binary responses are subject to classification errors. The focus of the paper is to study the effects of these errors on the estimates of the model parameters and also to propose a model that incorporates both these errors. The proposed model results in a substantial improvement in the estimates as shown by extensive simulation studies.     

Efficient Repeated Implementation

This paper examines repeated implementation of a social choice function (SCF) with infinitely lived agents whose preferences are determined randomly in each period. An SCF is repeatedly implementable in Nash equilibrium if there exists a sequence of (possibly history‐dependent) mechanisms such that its Nash equilibrium set is nonempty and every equilibrium outcome path results in the desired social choice at every possible history of past play and realizations of uncertainty. We show, with minor qualifications, that in the complete information environment an SCF is repeatedly implementable in Nash equilibrium if and only if it is efficient. We also discuss several extensions of our analysis.     

Reducing Surgical Ward Congestion Through Improved Surgical Scheduling and Uncapacitated Simulation

High surgical bed occupancy levels often result in heightened staff stress, frequent surgical cancellations, and long surgical wait times. This congestion is in part attributable to surgical scheduling practices, which often focus on the efficient use of operating rooms but ignore resulting downstream bed utilization. This paper describes a transparent and portable approach to improve scheduling practices, which combines a Monte Carlo simulation model and a mixed integer programming (MIP) model. For a specified surgical schedule, the simulation samples from historical case records and predicts bed requirements assuming no resource constraints. The MIP model complements the simulation model by scheduling both surgeon blocks and patient types to reduce peak bed occupancies. Scheduling guidelines were developed from the optimized schedules to provide surgical planners with a simple and implementable alternative to the MIP model. This approach has been tested and delivered to planners in a health authority in British Columbia, Canada. The models have been used to propose new surgical schedules and to evaluate the impact of proposed system changes on ward congestion.     

Power and sample size for GEE analysis of incomplete paired outcomes in 2 × 2 crossover trials

The 2 × 2 crossover trial uses subjects as their own control to reduce the intersubject variability in the treatment comparison, and typically requires fewer subjects than a parallel design. The generalized estimating equations (GEE) methodology has been commonly used to analyze incomplete discrete outcomes from crossover trials. We propose a unified approach to the power and sample size determination for the Wald Z-test and t-test from GEE analysis of paired binary, ordinal and count outcomes in crossover trials. The proposed method allows misspecification of the variance and correlation of the outcomes, missing outcomes, and adjustment for the period effect. We demonstrate that misspecification of the working variance and correlation functions leads to no or minimal efficiency loss in GEE analysis of paired outcomes. In general, GEE requires the assumption of missing completely at random. For bivariate binary outcomes, we show by simulation that the GEE estimate is asymptotically unbiased or only minimally biased, and the proposed sample size method is suitable under missing at random (MAR) if the working correlation is correctly specified. The performance of the proposed method is illustrated with several numerical examples. Adaption of the method to other paired outcomes is discussed.     

Pricing and Logistics Decisions for a Private‐Sector Provider in the Cash Supply Chain

We study a problem faced by a secure‐logistics provider (SLP) of maximizing profit by jointly pricing the services of fit‐sorting and transporting cash along with the design of the supporting logistics network, in a market consisting of a population of Depository Institutions (DIs). The need to jointly price the services assumes significance because they are partial substitutes of one another. Our study finds that the influence of the logistics network on prices is especially strong when there are non‐linearities in the cost of provisioning the logistics services. Furthermore, the impact of logistics decisions on different types of pricing schemes (e.g., volume discount, bundled pricing) is different, both in its structure and extent. In present times, when the market for the fit‐sorting service is relatively immature, our findings have major implications to the way an SLP's business is managed.     

油脂酸价测定实验中的浑浊现象及对策

油脂酸价测定实验中经常会遇到的样品溶液乳化浑浊问题,对产生这种现象的原因进行探讨,给出了这一现象产生的理论依据,阐述了如何避免乳化,确保酸价测定结果的准确性的应对措施.     

《暗算》叙述方式的解读

《暗算》在英雄主题、叙述策略、语言的组织上,都对当下军旅文学的审美视野进行了挑战。英雄的私语化描述,饱含情绪的主观化叙述,正奇语言的混杂,都给读者带来了新的审美感受。为此《暗算》也带来了迎合世俗文化消费的质疑,但新的叙述方式为军旅文学创作模式的多元化提供了借鉴和思索。     

评分方法在大学英语写作形成性评估中效应实证研究——整体评分与混合评分方法对比分析

基于形成性评价工具Writing Roadmap,针对评定学生写作文本时所采用的评分方式,分析在形成性评估过程中混合法和单纯的整体法评分方法对学习者写作水平提高的影响程度。实验对象为63名大学英语学习者。研究方法为观察法和量化统计法,并使用SPSS统计软件对写作成绩进行对比分析。研究表明,在大学英语写作形成性评估过程中,使用分项式和整体评分相结合的混合评分法更有助于促进学习者的写作水平的提高,并据此提出在写作形成性评估中设定混合评分标准应考虑的因素。     

Adaptive fitting of linear mixed-effects models with correlated random effects

Linear mixed-effects model has been widely used in longitudinal data analyses. In practice, the fitting algorithm can fail to converge due to boundary issues of the estimated random-effects covariance matrix G, that is, being near-singular, non-positive definite, or both. Current available algorithms are not computationally optimal because the condition number of matrix G is unnecessarily increased when the random-effects correlation estimate is not zero. We propose an adaptive fitting (AF) algorithm using an optimal linear transformation of the random-effects design matrix. It is a data-driven adaptive procedure, aiming at reducing subsequent random-effects correlation estimates down to zero in the optimal transformed estimation space. Simulations show that AF significantly improves the convergent properties, especially under small sample size, relative large noise and high correlation settings. One real data for insulin-like growth factor protein is used to illustrate the application of this algorithm implemented with software package R (nlme).     

General ridge predictors in a mixed linear model

This paper mainly aims to put forward two estimators for the linear combination of fixed effects and random effects, and to investigate their properties in a general mixed linear model. First, we define the notion of a Type-I general ridge predictor (GRP) and obtain two sufficient conditions for a Type-I GRP to be superior over the best linear unbiased predictor (BLUP). Second, we establish the relationship between a Type-I GRP and linear admissibility, which results in the notion of Type-II GRP. We show that a linear predictor is linearly admissible if and only if it is a Type-II GRP. The superiority of a Type-II GRP over the BLUP is also obtained. Third, the problem of confidence ellipsoids based on the BLUP and Type-II GRP is investigated.