On the α-risks for shewhart control charts

The performance of the usual Shewhart control charts for monitoring process means and variation can be greatly affected by nonnormal data or subgroups that are correlated. Define the α_k-risk for a Shewhart chart to be the probability that at least one “out-of-control” subgroup occurs in k subgroups when the control limits are calculated from the k subgroups. Simulation results show that the α_k-risks can be quite large even for a process with normally distributed, independent subgroups. When the data are nonnormal, it is shown that the α_k-risk increases dramatically. A method is also developed for simulating an “in-control” process with correlated subgroups from an autoregressive model. Simulations with this model indicate marked changes in the α_k-risks for the Shewhart charts utilizing this type of correlated process data. Therefore, in practice a process should be investigated thoroughly regarding whether or not it is generating normal, independent data before out-of-control points on the control charts are interpreted to be due to some real assignable cause.     

On sequential closed testing dose groups with a control

Methods for a sequential test of a dose-response effect in pre-clinical studies are investigated. The objective of the test procedure is to compare several dose groups with a zero-dose control. The sequential testing is conducted within a closed family of one-sided tests. The procedures investigated are based on a monotonicity assumption. These closed procedures strongly control the familywise error rate while providing information about the shape of the dose-responce relationship. Performance of sequential testing procedures are compared via a Monte Carlo simulation study. We illustrae the procedures by application to a real data set.     

Stop the Beat: Quiet Regulation and Cultural Conflict1

This article explores a recent conflict over the youth phenomenon known as “raving” in the City of Chicago. By interviewing participants involved in the conflict, I set out to understand the extent to which the crackdown on raves in Chicago was similar to earlier social reactions to jazz, comic books, rock and roll, and Dead Head culture, as well as to more recent conflicts over punk, rap music, and raves in other cities. While most previous research on cultural conflict has focused on moral crusades, campaigns, and panics, the Chicago conflict represents an example of “quiet regulation.” Opponents refrained from highly visible, morally charged attacks; instead, in the absence of media hype and visible public discourse, public officials justified the crackdown in highly bureaucratic terms—avoiding risk, collecting taxes, enforcing codes. Nonetheless, they also drew on cultural schemas that linked raves and raving with drugs, sex, and deviance. As a result, officials selected a course of regulation that criminalized DJs and discredited the artform. In the absence of a highly visible moral campaign, the rave participants were unable to mobilize and resist the regulation and defend their lifestyle. Sociologists must move beyond highly visible campaigns and crusades and pay greater attention to quiet regulation, both because such regulation is likely to increase in the future and because it has significant consequences for power, cultural expression, and identity.     

Power and dependence perspectives on outsourcing decisions

Since the 1980s outsourcing has been a major topic in both economic and management literature. Economic literature tends to focus on the transaction cost perspective. In management literature the core competence approach prevails. An emerging alternative view on outsourcing is the power and dependence perspective. Aspects of power and dependence become visible in four case studies involving the maintenance of capital assets. In each instance both the buyer and supplier sides of the dyad are examined. The paper concludes with managerial implications and research opportunities.     

Graphical and Numerical Methods for Detecting Influential Observations in Complex Bingham Data

We describe methods to detect influential observations in a sample of pre-shapes when the underlying distribution is assumed to be complex Bingham. One of these methods is based on Cook's distance, which is derived from the likelihood of the complex Bingham distribution. Other method is related to the tangent space, which is based on the local influence for the multivariate normal distribution. A method to detect outliers is also explained. The application of the methods is illustrated in both a real dataset and a simulated sample.     

The Multi-dimensional Characteristics of Wellbeing: How Different Aspects of Wellbeing Interact and Do Not Interact with Each Other

Because wellbeing is a multi-varied and dynamic phenomenon, social scientists need to better understand how different aspects of people’s wellbeing are tied together and how these ties differ between individuals at one point in time and within individuals over time. The paper outlines a general model that considers the multidimensional structure of wellbeing. We utilize the potential of latent variable modelling on a unique Swedish database: the Panel Survey of Ageing and the Elderly (PSAE). An inclusive and flexible model that considers the interactions between semi-autonomous domains (material resources, health, psychosocial factors, lifestyle, etc.) is developed. Our empirical study is based on cross-sectional PSAE data and focuses on people 55 years or older (N = 5,374). The analysis takes advantage of recent developments in statistical theory in the field of latent variable modelling to bring about a more dynamic and theory confirming analysis of a multifaceted phenomenon such as wellbeing.     

Improving TAIEX forecasting using fuzzy time series with Box–Cox power transformation

Box–Cox together with our newly proposed transformation were implemented in three different real world empirical problems to alleviate noisy and the volatility effect of them. Consequently, a new domain was constructed. Subsequently, universe of discourse for transformed data was established and an approach for calculating effective length of the intervals was then proposed. Considering the steps above, the initial forecasts were performed using frequently used fuzzy time series (FTS) methods on transformed data. Final forecasts were retrieved from initial forecasted values by proper inverse operation. Comparisons of the results demonstrate that the proposed method produced more accurate forecasts compared with existing FTS on original data.     

Effect of a new selection procedure on adaptive estimation

Adaptive estimation of parameters of some failure time distributionsis considered. A new procedure named the F-procedure has beendeveloped for selecting an appropriate model out of two possible models by Pandey et.al. (1991). Applying this F-procedure adaptive estimatorsof parameters of exponential, Wei bull, inverse Gaussian (IG) and Wald failure time distributions have been proposed in this paper. Comparison of these estimators has been undertaken with MLE's of the respective parameters and with some previous adaptiveestimators by simulation of samples using the Monte Carlo method.Adaptive estimation of parameters of some failure time distributions is considered. A new procedure named the F-procedure has been developedfor selecting an appropriate model out of two possible models by Pandey et.al. (1991). Applying this F-procedure adaptive estimators of parameters of exponential, Wei bull, inverse Gaussian (IG) and Wald failure time distributions have been proposed in this paper. Comparison of these estimators has been undertaken with MLE's of the respective parameters and with some previous adaptive estimators by simulation of samples using the Monte Carlo method.