Phase II statistical process control for functional data

In the manufacturing process, a sequence of measurements of quality characteristic is increasingly taken across some continuum, producing a curve that represents the quality of the item. This curve provides the so-called profile or functional data. Regardless of a linear or nonlinear profile, the common approaches of the control chart are based on the multivariate control chart by monitoring the estimated parameter of the pre-defined linear or nonlinear model. Usually, the model is difficult to know practically, and it is also difficult to identify the abnormal pattern from the outlying parameter. The functional data control chart we propose can provide a better solution to these problems. In the Monte-Carlo simulations, we show that the functional data control chart is sensitive when the underlying process status is changed. By applying the vertical density profile data, the new method exhibits a good performance.     

基于公平理论探讨服务补救质量影响的实证研究——来自中国电信服务行业的证据

企业与客户交易时并不会一帆风顺,当客户面对交易过程中出现的故障时,不同服务补救措施会如何对客户感知、满意度和信任度产生影响?本文区别于以往实验或准实验法为主的定性或定量研究,引入组织行为学的公平理论,采取真实客户服务数据,基于不同客户类型深入探讨服务补救质量对客户满意、客户信任和客户忠诚的影响.实证研究发现,服务补救质量对感知价值、满意和信任均会产生正向作用;大客户对服务补救过程中的"互动公平"最为重视,公众客户更为强调"结果公平"的影响.这就告诉服务型企业应如何采取补救措施,大客户看重过程,一般客户更看重结果.本文从理论和实践两方面为服务补救领域的研究提供了有力证据.     

Queue hurdle Coxian phase-type model for two-stage process of population-based cancer screening

Statistical Methods & Applications - The quality assurance of two-stage population-based cancer screening program is determined by arrival rate (attending screening), positive rate (determined...     

A Comparison of Selected Artificial Neural Networks that Help Auditors Evaluate Client Financial Viability

This study compares the performance of three artificial neural network (ANN) approaches—backpropagalion, categorical learning, and probabilistic neural network—as classification tools to assist and support auditor's judgment about a client's continued financial viability into the future (going concern status). ANN performance is compared on the basis of overall error rates and estimated relative costs of misclassificaticn (incorrectly classifying an insolvent firm as solvent versus classifying a solvent firm as insolvent). When only the overall error rate is considered, the probabilistic neural network is the most reliable in classification, followed by backpropagation and categorical learning network. When the estimated relative costs of misclassification are considered, the categorical learning network is the least costly, followed by backpropagation and probabilistic neural network.