Bootstrap-Based T 2 Multivariate Control Charts

Control charts have been used effectively for years to monitor processes and detect abnormal behaviors. However, most control charts require a specific distribution to establish their control limits. The bootstrap method is a nonparametric technique that does not rely on the assumption of a parametric distribution of the observed data. Although the bootstrap technique has been used to develop univariate control charts to monitor a single process, no effort has been made to integrate the effectiveness of the bootstrap technique with multivariate control charts. In the present study, we propose a bootstrap-based multivariate T ² control chart that can efficiently monitor a process when the distribution of observed data is nonnormal or unknown. A simulation study was conducted to evaluate the performance of the proposed control chart and compare it with a traditional Hotelling's T ² control chart and the kernel density estimation (KDE)-based T ² control chart. The results showed that the proposed chart performed better than the traditional T ² control chart and performed comparably with the KDE-based T ² control chart. Furthermore, we present a case study to demonstrate the applicability of the proposed control chart to real situations.     

Performance analysis of re-entrant flow shop with single-job and batch machines using mean value analysis

We propose an approximate method based on the mean value analysis for estimating the average performance of re-entrant flow shop with single-job machines and batch machines. The main focus is on the steady-state averages of the cycle time and the throughput of the system. Characteristics of the re-entrant flow and inclusion of the batch machines complicate the exact analysis of the system. Thus, we propose an approximate analytic method for obtaining the mean waiting time at each buffer of the workstation and a heuristic method to improve the result of the analytic method. We compare the results of the proposed approach with a simulation study using some numerical examples.     

New employee orientation: cases of Korean corporations

Despite its effectiveness in socialization and development, new employee orientation (NEO) as a stand-alone training intervention has rarely been a topic of interest in the academic world. The purpose of this study was to examine the NEO programmes for university graduates in Korean corporations. Through an extensive review of NEO-related materials in 10 large Korean corporations, the designs and contents of Korean NEOs were analysed. As a result, all investigated corporations were found to use most of the institutionalized socialization tactics: formal, collective, fixed, sequential, and serial. The contents of the NEOs mainly focused on the synergism and integration roles. The embedded national culture and organizational contexts in Korean corporations were explored to explain the practices of the NEOs. Implications for HRD practice and research were suggested.     

原产国效应与原产地效应的实证研究--中韩比较

本研究在国际营销研究领域里比较广泛探讨的原产国效应(Country of Origin Effect)的基础上,分析了中国消费者在评价国内外品牌时所产生的原产地效应(Region of Origin Effect)问题.根据韩国/中国和上海/沈阳的原产国和原产地效应的实证分析,发现中国消费者评价韩国或中国产品时不受原产国效应的影响,但却受到原产地效应影响,这是本研究的重要发现,也对企业管理实践具有重要意义.     

Nonstationary Binary Choice

This paper develops an asymptotic theory for time series binary choice models with nonstationary explanatory variables generated as integrated processes. Both logit and probit models are covered. The maximum likelihood (ML) estimator is consistent but a new phenomenon arises in its limit distribution theory. The estimator consists of a mixture of two components, one of which is parallel to and the other orthogonal to the direction of the true parameter vector, with the latter being the principal component. The ML estimator is shown to converge at a rate of n^3/4 along its principal component but has the slower rate of n^1/4 convergence in all other directions. This is the first instance known to the authors of multiple convergence rates in models where the regressors have the same (full rank) stochastic order and where the parameters appear in linear forms of these regressors. It is a consequence of the fact that the estimating equations involve nonlinear integrable transformations of linear forms of integrated processes as well as polynomials in these processes, and the asymptotic behavior of these elements is quite different. The limit distribution of the ML estimator is derived and is shown to be a mixture of two mixed normal distributions with mixing variates that are dependent upon Brownian local time as well as Brownian motion. It is further shown that the sample proportion of binary choices follows an arc sine law and therefore spends most of its time in the neighborhood of zero or unity. The result has implications for policy decision making that involves binary choices and where the decisions depend on economic fundamentals that involve stochastic trends. Our limit theory shows that, in such conditions, policy is likely to manifest streams of little intervention or intensive intervention.