基于广义模糊数的软件成本加权CBR估算研究

在软件项目开发过程中,准确估算出软件成本在提高软件质量和保障软件成功开发方面起到重要支撑作用。针对软件项目历史数据库中部分属性在项目开发初期难以给予精确数值(仅仅能给出模糊数),而已有软件成本估算模型不能很好地处理模糊信息的问题,本文在基于案例推理模型(CBR)基础上集成广义模糊数,提出了基于广义模糊数的CBR模型。使用基于广义模糊数的相似度度量方法代替传统CBR模型中采用的欧式距离等相似度度量方法,采用模糊C均值聚类(FCM)方法将已有软件项目历史数据库中的精确数值进行模糊化处理,以匹配新项目中的模糊数。进一步采用粒子群算法(PSO)来优化属性的权重,构建基于广义模糊数的加权CBR模型。最终在实验中采用Desharnais数据来检验构建模型的有效性。实证结果表明,在与常用的欧式距离CBR模型相比,构建的基于广义模糊数的加权CBR模型能有效提高估算精度,采用PSO优化属性权重能提高模型的估算精度。     

Nonparametric Bayesian Methods for Benchmark Dose Estimation

The article proposes and investigates the performance of two Bayesian nonparametric estimation procedures in the context of benchmark dose estimation in toxicological animal experiments. The methodology is illustrated using several existing animal dose‐response data sets and is compared with traditional parametric methods available in standard benchmark dose estimation software (BMDS), as well as with a published model‐averaging approach and a frequentist nonparametric approach. These comparisons together with simulation studies suggest that the nonparametric methods provide a lot of flexibility in terms of model fit and can be a very useful tool in benchmark dose estimation studies, especially when standard parametric models fail to fit to the data adequately.     

Estimation of Jump Tails

We propose a new and flexible nonparametric framework for estimating the jump tails of Itô semimartingale processes. The approach is based on a relatively simple‐to‐implement set of estimating equations associated with the compensator for the jump measure, or its intensity, that only utilizes the weak assumption of regular variation in the jump tails, along with in‐fill asymptotic arguments for directly estimating the “large” jumps. The procedure assumes that the large‐sized jumps are identically distributed, but otherwise allows for very general dynamic dependencies in jump occurrences, and, importantly, does not restrict the behavior of the “small” jumps or the continuous part of the process and the temporal variation in the stochastic volatility. On implementing the new estimation procedure with actual high‐frequency data for the S&P 500 aggregate market portfolio, we find strong evidence for richer and more complex dynamic dependencies in the jump tails than hitherto entertained in the literature.     

Lower Risk Bounds and Properties of Confidence Sets for Ill‐Posed Estimation Problems with Applications to Spectral Density and Persistence Estimation,Unit Roots,and Estimation of Long Memory Parameters

Important estimation problems in econometrics like estimating the value of a spectral density at frequency zero, which appears in the econometrics literature in the guises of heteroskedasticity and autocorrelation consistent variance estimation and long run variance estimation, are shown to be “ill‐posed” estimation problems. A prototypical result obtained in the paper is that the minimax risk for estimating the value of the spectral density at frequency zero is infinite regardless of sample size, and that confidence sets are close to being uninformative. In this result the maximum risk is over commonly used specifications for the set of feasible data generating processes. The consequences for inference on unit roots and cointegration are discussed. Similar results for persistence estimation and estimation of the long memory parameter are given. All these results are obtained as special cases of a more general theory developed for abstract estimation problems, which readily also allows for the treatment of other ill‐posed estimation problems such as, e.g., nonparametric regression or density estimation.     

A MARKET UTILITY‐BASED MODEL FOR CAPACITY SCHEDULING IN MASS SERVICES

Only a small set of employee scheduling articles have considered an objective of profit or contribution maximization, as opposed to the traditional objective of cost (including opportunity costs) minimization. In this article, we present one such formulation that is a market utility‐based model for planning and scheduling in mass services (MUMS). MUMS is a holistic approach to market‐based service capacity scheduling. The MUMS framework provides the structure for modeling the consequences of aligning competitive priorities and service attributes with an element of the firm's service infrastructure. We developed a new linear programming formulation for the shift‐scheduling problem that uses market share information generated by customer preferences for service attributes. The shift‐scheduling formulation within the framework of MUMS provides a business‐level model that predicts the economic impact of the employee schedule. We illustrated the shift‐scheduling model with empirical data, and then compared its results with models using service standard and productivity standard approaches. The result of the empirical analysis provides further justification for the development of the market‐based approach. Last, we discuss implications of this methodology for future research.     

Beyond Patient Classification: Using Individual Patient Characteristics in Appointment Scheduling

Scheduling patients involves a trade‐off between the productivity of the service provider and customer service. This study considers how outpatient medical facilities can improve their appointment scheduling by incorporating individual patient information in the scheduling process. Specifically, we obtain data on patient characteristics and examination durations from a health clinic, describe how that data can be used to predict patient examination durations in the clinic's appointment scheduling system, and evaluate the benefit of using individual patient characteristics over a conventional classification method. Computational results illustrate this method of patient scheduling reduces an overall cost function comprised of patient wait time, physician idle time, and over time by up to 24.2%, particularly when patients are sequenced with short duration patients being scheduled first. Several environmental characteristics are found to play critical roles in determining the magnitude of the benefit, including patient punctuality, no‐show probability, the clinic duration, the appointment rule used for scheduling, and the ratio of the physician's idle time cost to the patient wait cost. We also detail and evaluate a practical procedure for using heterogeneous scheduling under a fixed schedule.     

Bi‐Criteria Scheduling of Surgical Services for an Outpatient Procedure Center

Uncertainty in the duration of surgical procedures can cause long patient wait times, poor utilization of resources, and high overtime costs. We compare several heuristics for scheduling an Outpatient Procedure Center. First, a discrete event simulation model is used to evaluate how 12 different sequencing and patient appointment time‐setting heuristics perform with respect to the competing criteria of expected patient waiting time and expected surgical suite overtime for a single day compared with current practice. Second, a bi‐criteria genetic algorithm (GA) is used to determine if better solutions can be obtained for this single day scheduling problem. Third, we investigate the efficacy of the bi‐criteria GA when surgeries are allowed to be moved to other days. We present numerical experiments based on real data from a large health care provider. Our analysis provides insight into the best scheduling heuristics, and the trade‐off between patient and health care provider‐based criteria. Finally, we summarize several important managerial insights based on our findings.     

一类稀疏投资组合双层参数估计模型及其应用

带基约束的投资组合问题是近年来投资组合领域的热点问题,但是参数不确定性直接影响了模型的效果。带基约束的投资组合问题所涉及的参数不仅包括以往研究认为非常重要的预期收益率,还包括控制投资组合规模的稀疏度,尤其是最优稀疏度估计方面的专门研究还十分匮乏。为了使带基约束的投资组合模型更好地为投资决策服务,本文从投资者效用出发,用双层规划的思想构建了带基约束的投资组合双层参数估计模型。然后根据模型的特点,设计了无导数优化算法框架,并基于ADMM对算法子问题进行求解。本文实验针对真实的市场数据给出了预期收益率和最优稀疏度的估计,接着通过与等权重策略和含上下界约束的均值-方差模型进行比较,说明了模型及算法的有效性和实用性。最后,将本文提出的双层参数估计模型推广到了更一般的形式。     

Reexamining the Nurse Scheduling Problem: Staffing Ratios and Nursing Shortages*

Legislators at the state and national levels are addressing renewed concerns over the adequacy of hospital nurse staffing to provide quality care and ensure patient safety. At the same time, the well‐known nursing shortage remains an ongoing problem. To address these issues, we reexamine the nurse scheduling problem and consider how recent health care legislation impacts nursing workforce management decisions. Specifically, we develop a scheduling model and perform computational experiments to evaluate how mandatory nurse‐to‐patient ratios and other policies impact schedule cost and schedule desirability (from the nurses' perspective). Our primary findings include the following: (i) nurse wage costs can be highly nonlinear with respect to changes in mandatory nurse‐to‐patient ratios of the type being considered by legislators; (ii) the number of undesirable shifts can be substantially reduced without incurring additional wage cost; (iii) more desirable scheduling policies, such as assigning fewer weekends to each nurse, have only a small impact on wage cost; and (iv) complex policy statements involving both single‐period and multiperiod service levels can sometimes be relaxed while still obtaining good schedules that satisfy the nurse‐to‐patient ratio requirements. The findings in this article suggest that new directions for future nurse scheduling models, as it is likely that nurse‐to‐patient ratios and nursing shortages will remain a challenge for health care organizations for some time.     

基于门限双幂变差的资产价格时点波动非参数估计

估计带跳资产价格的时点波动时,需要用门限过滤方法消除跳的影响。在有限样本下,门限过滤会产生错滤偏误和漏虑偏误,降低估计精度。跳错滤产生的偏误可通过对错滤样本进行补足的方法进行纠偏,但由于发生时点未知,跳漏滤产生的偏误无法纠正,只能通过估计量设计来减少漏滤偏误。本文首次提出基于门限双幂变差的时点波动估计量,采用核平滑方法对资产价格时点波动进行非参数估计,有效减少跳错滤导致的偏误。采用随机阵列极限理论,本文证明了估计量的一致性和渐进正态性,在分析有限样本偏误的基础上,给出估计量的纠偏方法。蒙特卡洛模拟表明,本文给出的估计量,漏滤偏误明显小于基于二次变差构造的估计量,对时点波动估计的性质具有实质改进。采用Kupiec动态VaR精度检验对沪深300指数的实证分析表明,本文给出的时点波动估计更能描述资产收益的波动特征。     

Sample Splitting and Threshold Estimation

Threshold models have a wide variety of applications in economics. Direct applications include models of separating and multiple equilibria. Other applications include empirical sample splitting when the sample split is based on a continuously‐distributed variable such as firm size. In addition, threshold models may be used as a parsimonious strategy for nonparametric function estimation. For example, the threshold autoregressive model (TAR) is popular in the nonlinear time series literature. Threshold models also emerge as special cases of more complex statistical frameworks, such as mixture models, switching models, Markov switching models, and smooth transition threshold models. It may be important to understand the statistical properties of threshold models as a preliminary step in the development of statistical tools to handle these more complicated structures. Despite the large number of potential applications, the statistical theory of threshold estimation is undeveloped. It is known that threshold estimates are super‐consistent, but a distribution theory useful for testing and inference has yet to be provided. This paper develops a statistical theory for threshold estimation in the regression context. We allow for either cross‐section or time series observations. Least squares estimation of the regression parameters is considered. An asymptotic distribution theory for the regression estimates (the threshold and the regression slopes) is developed. It is found that the distribution of the threshold estimate is nonstandard. A method to construct asymptotic confidence intervals is developed by inverting the likelihood ratio statistic. It is shown that this yields asymptotically conservative confidence regions. Monte Carlo simulations are presented to assess the accuracy of the asymptotic approximations. The empirical relevance of the theory is illustrated through an application to the multiple equilibria growth model of Durlauf and Johnson (1995).     

沪深300指数跳的逐点检验及动态分析

作为金融衍生产品标的,沪深300指数是否存在跳以及跳服从怎样的动态规律对资产定价和风险管理都十分重要。本文提出新的时点方差估计方法,构造渐进性质更好的跳检验统计量,以此对沪深300指数5分钟数据进行时点跳检验,在此基础上对跳的动态变化进行分析。实证结果表明,沪深300指数存在跳,跳发生次数服从Poisson过程,但跳发生概率随时间变化,具有时变性;跳幅分布具有厚尾性并向右偏斜,分布随时间发生变化,不服从同分布假设。本文的研究结果为相关研究提供了基础性实证结论。     

Simulation Modeling and Analysis: A Collateral Application and Exposition of RFID Technology

RFID technology provides in‐depth, real‐time visibility into the status of assets throughout the supply chain. However, the deployment of RFID technology may have collateral value in the high‐quality data generated by these assets. This study explores the potential value of RFID data for tactical and strategic purposes and the redesign of processes within supply chain through the deployment of simulation modeling and analysis. We present a simulation study conducted at a regional hospital for which data related to trauma patient movement was collected with an RFID‐based system. We find that not only does this data serve as the basis for successful simulation modeling, but that RFID technology may address several data‐related challenges previously identified in the simulation literature.     

On‐Call Overtime for Service Workforce Scheduling when Demand Is Uncertain

This article builds on prior research to develop shift scheduling models that include on‐call overtime for service environments where demand is uncertain. The research is motivated by recent developments in nurse scheduling, such as laws prohibiting mandatory overtime and the popularity of self‐scheduling systems. For single‐period scenarios, models are developed, solution methods are described, and results are explored for a variety of environments. Results show that the use of on‐call overtime can reduce costs slightly, with the amount of savings dependent on characteristics of the scheduling environment. The factor that most significantly affects cost savings is the cost of outside agency workers relative to overtime workers. In addition to lowering costs, on‐call overtime greatly reduces reliance on outside agency workers, which can have important practical implications in terms of quality of service and workforce morale. Results based on single‐period models motivate multiperiod formulations for single‐ and multidepartment scenarios, and solution methods are outlined for those cases. The possibility of using multiperiod models within a rolling horizon framework with forecast updating is discussed. This goes along with an extension of the traditional workforce management hierarchy that separates overtime and regular‐time scheduling, as seen in practice with self‐scheduling and shift‐bidding systems.     

OUTPATIENT SCHEDULING IN HEALTH CARE: A REVIEW OF LITERATURE

This paper provides a comprehensive survey of research on appointment scheduling in outpatient services. Effective scheduling systems have the goal of matching demand with capacity so that resources are better utilized and patient waiting times are minimized. Our goal is to present general problem formulation and modeling considerations, and to provide taxonomy of methodologies used in previous literature. Current literature fails to develop generally applicable guidelines to design appointment systems, as most studies have suggested highly situation‐specific solutions. We identify future research directions that provide opportunities to expand existing knowledge and close the gap between theory and practice.     

A Common Rationale for Global Sensitivity Measures and Their Estimation

Measures of sensitivity and uncertainty have become an integral part of risk analysis. Many such measures have a conditional probabilistic structure, for which a straightforward Monte Carlo estimation procedure has a double‐loop form. Recently, a more efficient single‐loop procedure has been introduced, and consistency of this procedure has been demonstrated separately for particular measures, such as those based on variance, density, and information value. In this work, we give a unified proof of single‐loop consistency that applies to any measure satisfying a common rationale. This proof is not only more general but invokes less restrictive assumptions than heretofore in the literature, allowing for the presence of correlations among model inputs and of categorical variables. We examine numerical convergence of such an estimator under a variety of sensitivity measures. We also examine its application to a published medical case study.     

Model Selection and Estimation with Quantal‐Response Data in Benchmark Risk Assessment

This article describes several approaches for estimating the benchmark dose (BMD) in a risk assessment study with quantal dose‐response data and when there are competing model classes for the dose‐response function. Strategies involving a two‐step approach, a model‐averaging approach, a focused‐inference approach, and a nonparametric approach based on a PAVA‐based estimator of the dose‐response function are described and compared. Attention is raised to the perils involved in data “double‐dipping” and the need to adjust for the model‐selection stage in the estimation procedure. Simulation results are presented comparing the performance of five model selectors and eight BMD estimators. An illustration using a real quantal‐response data set from a carcinogenecity study is provided.     

“特质波动率之谜”与估计模型有关吗？

以CAPM、Fama-French三因子和五因子模型为均值方程,分别采用无条件标准差,以及GARCH、EGARCH条件方差为基准计算出中国股票市场的特质波动率,研究了特质波动率与股票收益率之间的关系。研究发现,关于高特质波动风险对应低预期收益的定价异象来源于特质波动率估计模型的差异。具体地,当使用无条件标准差方式估计特质波动率时,存在定价异象,而使用GARCH、EGARCH等条件方差模型估计特质波动率时,则不存在定价异象。该结果在改变残差估计均值方程以及控制规模、流动性等其它变量后依然稳健。本文的研究有助于解释长期困扰在资产定价领域的“特质波动率之谜”。     

Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data

The benchmark dose (BMD) approach has gained acceptance as a valuable risk assessment tool, but risk assessors still face significant challenges associated with selecting an appropriate BMD/BMDL estimate from the results of a set of acceptable dose‐response models. Current approaches do not explicitly address model uncertainty, and there is an existing need to more fully inform health risk assessors in this regard. In this study, a Bayesian model averaging (BMA) BMD estimation method taking model uncertainty into account is proposed as an alternative to current BMD estimation approaches for continuous data. Using the “hybrid” method proposed by Crump, two strategies of BMA, including both “maximum likelihood estimation based” and “Markov Chain Monte Carlo based” methods, are first applied as a demonstration to calculate model averaged BMD estimates from real continuous dose‐response data. The outcomes from the example data sets examined suggest that the BMA BMD estimates have higher reliability than the estimates from the individual models with highest posterior weight in terms of higher BMDL and smaller 90th percentile intervals. In addition, a simulation study is performed to evaluate the accuracy of the BMA BMD estimator. The results from the simulation study recommend that the BMA BMD estimates have smaller bias than the BMDs selected using other criteria. To further validate the BMA method, some technical issues, including the selection of models and the use of bootstrap methods for BMDL derivation, need further investigation over a more extensive, representative set of dose‐response data.     

Estimation of a Benchmark Dose in the Presence or Absence of Hormesis Using Posterior Averaging

U.S. Environment Protection Agency benchmark doses for dichotomous cancer responses are often estimated using a multistage model based on a monotonic dose‐response assumption. To account for model uncertainty in the estimation process, several model averaging methods have been proposed for risk assessment. In this article, we extend the usual parameter space in the multistage model for monotonicity to allow for the possibility of a hormetic dose‐response relationship. Bayesian model averaging is used to estimate the benchmark dose and to provide posterior probabilities for monotonicity versus hormesis. Simulation studies show that the newly proposed method provides robust point and interval estimation of a benchmark dose in the presence or absence of hormesis. We also apply the method to two data sets on carcinogenic response of rats to 2,3,7,8‐tetrachlorodibenzo‐p‐dioxin.