Outlier detection using difference-based variance estimators in multiple regression

In this article, we propose an outlier detection approach in a multiple regression model using the properties of a difference-based variance estimator. This type of a difference-based variance estimator was originally used to estimate error variance in a non parametric regression model without estimating a non parametric function. This article first employed a difference-based error variance estimator to study the outlier detection problem in a multiple regression model. Our approach uses the leave-one-out type method based on difference-based error variance. The existing outlier detection approaches using the leave-one-out approach are highly affected by other outliers, while ours is not because our approach does not use the regression coefficient estimator. We compared our approach with several existing methods using a simulation study, suggesting the outperformance of our approach. The advantages of our approach are demonstrated using a real data application. Our approach can be extended to the non parametric regression model for outlier detection.     

How to build nomogram for type 2 diabetes using a naïve Bayesian classifier technique

In this study, we introduced a method for building a Bayesian nomogram and proposed an appropriate nomogram for type 2 diabetes (T2D) using data from 13,474 subjects collected from the 2013–2015 Korean National Health and Nutrition Examination Survey (KNHANES) data. We identified risk factors related to T2D, proposed a visual nomogram for T2D from a naïve Bayesian classifier model, and predicted incidence rates. Additionally, we computed confidence intervals for the influence of risk factors (attributes) and verified the proposed Bayesian nomogram using a receiver operating characteristic curve. Finally, we compared logistic regression and the Bayesian nomogram for T2D. The results of the analysis of the T2D data showed that the most influential factor among all attributes in the Bayesian nomogram was age group, and the highest risk factor for T2D incidence was cardiovascular disease. Dyslipidemia and hypertension also had significant impacts on T2D incidence while the effects of sex, smoking status, and employment status were relatively small compared to those of other variables. Using the proposed Bayesian nomogram, we can easily predict the incidence rate of T2D in an individual, and treatment plans can be established based on this information.     

E-journal Management Systems

Abstract

Electronic journals are being added to library collections at exponential rates. Recent developments in e-journal management systems offer libraries a variety of solutions for enhancing the visibility and flexibility of their electronic journals. This article examines and compares Serials Solutions, JournalWebCite, TDNet, and Serials Cybrarian, four current e-journal management programs that create and integrate multiple lists of journal titles, regardless of format, into a single list. These systems can reduce or even eliminate common serials control problems for librarians. Readers will gain insights that will enable them to better understand and consider the value of an e-journal management system. Serials Review 2002; 28:176–194.     

Using the dependent wild bootstrap for the nonparametric goodness-of-fit test for density functions

In this paper, we consider the well-known nonparametric consistent model-specification test for the stationary density function (see [Aït-Sahalia Y. Testing continuous-time models of the spot interest rate. Rev Financ Stud. 1996;9:385–426; Li Q. Nonparametric testing of closeness between two unknown distribution functions. Econ Rev. 1996;15:261–274; Fan Y, Ullah A. On goodness-of-fit tests for weakly dependent processes using kernel method. J Nonparametric Stat. 2000;11:337–360]) and reinvestigate it carefully using asymptotics and simulation. Our work reveals that the test is subject to power and size distortions, which are mainly caused by dependence or convergence rate changes under the null and alternative hypothesis. A dependent wild bootstrap is newly suggested as a feasible remedy to such distortions. Our result provides a complete explanation as well as a solution to the problem that experienced by Aït-Sahalia [Testing continuous-time models of the spot interest rate. Rev Financ Stud. 1996;9:385–426], that is, that the test rejects true models too often when independent and identically distributed asymptotic critical values are used.     

A non-stationary integer-valued autoregressive model

It is frequent to encounter a time series of counts which are small in value and show a trend having relatively large fluctuation. To handle such a non-stationary integer-valued time series with a large dispersion, we introduce a new process called integer-valued autoregressive process of order p with signed binomial thinning (INARS(p)). This INARS(p) uniquely exists and is stationary under the same stationary condition as in the AR(p) process. We provide the properties of the INARS(p) as well as the asymptotic normality of the estimates of the model parameters. This new process includes previous integer-valued autoregressive processes as special cases. To preserve integer-valued nature of the INARS(p) and to avoid difficulty in deriving the distributional properties of the forecasts, we propose a bootstrap approach for deriving forecasts and confidence intervals. We apply the INARS(p) to the frequency of new patients diagnosed with acquired immunodeficiency syndrome (AIDS) in Baltimore, Maryland, U.S. during the period of 108 months from January 1993 to December 2001.     

Compromise Allocation in Univariate Stratified Sampling

To reduce the loss of efficiency in the Neyman allocation caused by using the estimators instead of the unknown strata standard deviations of population, we suggest a compromise allocation that the Neyman allocation using an estimator of the pooled standard deviation of combined strata and the proportional allocation are used together. It is shown that the compromise allocation makes the estimator more efficient than the proportional allocation and the Neyman allocation using the estimated strata standard deviations. Simulation study is carried out for the numerical comparison and the results are reported.     

Assembly block storage location assignment at a shipyard: a case of Hyundai Heavy Industries

It is not a trivial issue how to manage assembly blocks at a shipyard. Based on the project experience in Hyundai Heavy Industries, currently the largest shipbuilding company in the world and the leader in the Korean merchant shipbuilding industry, this study points out the difficulties on the block stockyard operations, formalises the assembly block storage location assignment problem, and develops the assembly block storage location assignment algorithm, the purpose of which is to reduce the number of unproductive block moves. Through simulation experiments for various situations, this study demonstrates the usefulness of the assembly block storage location assignment algorithm. In addition, this study examines the impacts of block move sequence rules and of block stockyard layouts on the block stockyard operations.     

A missing link: psychological ownership as a mediator between transformational leadership and organizational citizenship behaviour

Interest in psychological ownership (PO) has increased due to its influence on employees’ attitudes and behaviours. This study focuses on PO as a mediator between transformational leadership (TL) and organizational citizenship behaviour (OCB) in the Korean public sector, which does not have employee stock ownership plans (ESOPs). With a total of 214 cases from the Korean public sector, structural equation modelling (SEM) analysis was conducted, and chi-square difference-based model comparison was performed to examine the mediating effect. Results indicated statistically significant relationships between TL and employees’ PO and between employees’ PO and their OCB. A statistically significant full mediating role of PO between TL and OCB in the Korean public sector context was found.     

Supply Chain Design and Carbon Penalty: Monopoly vs. Monopolistic Competition

This paper studies whether imposing carbon costs changes the supply chain structure and social welfare. We explore the problem from a central policymaker's perspective who wants to maximize social welfare. We consider two stakeholders, retailers, and consumers, who optimize their own objectives (i.e., profits and net utility) and three competitive settings (i.e., monopoly, monopolistic competition with symmetric market share, and monopolistic competition with asymmetric market share). For the monopoly case, we find that when the retailer's profit is high, imposing some carbon emission charges on the retailer and the consumers does not substantially change the supply chain structure or the social welfare. However, when the retailer's profit is low, imposing carbon costs optimally can lead to a significant increase in social welfare. Moreover, the impact of imposing carbon emission charges becomes more significant when the degree of competition increases. Additionally, the quantum of benefit may depend only on factors common across industries, such as fuel and carbon costs.