Constant Stress Accelerated Life Test on a Multiple-Component Series System under Weibull Lifetime Distributions

We will discuss the reliability analysis of the constant stress accelerated life test on a series system connected with multiple components under independent Weibull lifetime distributions whose scale parameters are log-linear in the level of the stress variable. The system lifetimes are collected under Type I censoring but the components that cause the systems to fail may or may not be observed. The data are so called masked for the latter case. Maximum likelihood approach and the Bayesian method are considered when the data are masked. Statistical inference on the estimation of the underlying model parameters as well as the mean time to failure and the reliability function will be addressed. Simulation study for a three-component case shows that Bayesian analysis outperforms the maximum likelihood approach especially when the data are highly masked.     

On the equitable k *-laceability of hypercubes

Let G be a finite undirected bipartite graph. Let u, v be two vertices of G from different partite sets. A collection of k internal vertex disjoint paths joining u to v is referred as a k-container C _k (u,v). A k-container is a k ^*-container if it spans all vertices of G. We define G to be a k ^*-laceable graph if there is a k ^*-container joining any two vertices from different partite sets. A k ^*-container C _k ^*(u,v)={P ₁,…,P _k } is equitable if ||V(P _i )|−|V(P _j )||≤2 for all 1≤i,j≤k. A graph is equitably k ^*-laceable if there is an equitable k ^*-container joining any two vertices in different partite sets. Let Q _n be the n-dimensional hypercube. In this paper, we prove that the hypercube Q _n is equitably k ^*-laceable for all k≤n−4 and n≥5. Dedicated to Professor Frank K. Hwang on the occasion of his 65th birthday. The work of H.-M. Huang was supported in part by the National Science Council of the Republic of China under NSC94-2115-M008-013.     

Adjusted Supremum Score‐Type Statistics for Evaluating Non‐Standard Hypotheses

Supremum score test statistics are often used to evaluate hypotheses with unidentifiable nuisance parameters under the null hypothesis. Although these statistics provide an attractive framework to address non‐identifiability under the null hypothesis, little attention has been paid to their distributional properties in small to moderate sample size settings. In situations where there are identifiable nuisance parameters under the null hypothesis, these statistics may behave erratically in realistic samples as a result of a non‐negligible bias induced by substituting these nuisance parameters by their estimates under the null hypothesis. In this paper, we propose an adjustment to the supremum score statistics by subtracting the expected bias from the score processes and show that this adjustment does not alter the limiting null distribution of the supremum score statistics. Using a simple example from the class of zero‐inflated regression models for count data, we show empirically and theoretically that the adjusted tests are superior in terms of size and power. The practical utility of this methodology is illustrated using count data in HIV research.     

The Extensively Corrected Score for Measurement Error Models

In measurement error problems, two major and consistent estimation methods are the conditional score and the corrected score. They are functional methods that require no parametric assumptions on mismeasured covariates. The conditional score requires that a suitable sufficient statistic for the mismeasured covariate can be found, while the corrected score requires that the object score function can be estimated without bias. These assumptions limit their ranges of applications. The extensively corrected score proposed here is an extension of the corrected score. It yields consistent estimations in many cases when neither the conditional score nor the corrected score is feasible. We demonstrate its constructions in generalized linear models and the Cox proportional hazards model, assess its performances by simulation studies and illustrate its implementations by two real examples.     

Green Purchasing by MNC Subsidiaries: The Role of Local Tailoring in the Presence of Institutional Duality

To examine the essential determinants of green purchasing by multinational corporations’ (MNC) subsidiaries, this study takes institutional theory as a foundation and focuses on the institutional duality associated with localization and globalization. Specifically, we develop a model to explain subsidiaries’ green purchasing and empirically test the model with data from 141 purchasing managers and senior purchasing staff members from subsidiaries in 39 countries. Our results suggest that pressures from headquarters and the local environment do not affect subsidiaries’ green purchasing directly; rather, they exert indirect influences through local tailoring. This study contributes to extant literature by revealing the significance of local tailoring in an MNC context. In addition, our findings offer several implications for practice by providing a roadmap for disseminating green purchasing across the subsidiaries of an MNC, as well as highlighting the importance of both clear communication about the benefits of green purchasing and internal audits.     

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.