Phase-II monitoring and diagnosing of multivariate categorical processes using generalized linear test-based control charts

In this paper, two control charts based on the generalized linear test (GLT) and contingency table are proposed for Phase-II monitoring of multivariate categorical processes. The performances of the proposed methods are compared with the exponentially weighted moving average-generalized likelihood ratio test (EWMA-GLRT) control chart proposed in the literature. The results show the better performance of the proposed control charts under moderate and large shifts. Moreover, a new scheme is proposed to identify the parameter responsible for an out-of-control signal. The performance of the proposed diagnosing procedure is evaluated through some simulation experiments.     

The economic design of multivariate binomial EWMA VSSI control charts

Since multi-attribute control charts have received little attention compared with multivariate variable control charts, this research is concerned with developing a new methodology to employ the multivariate exponentially weighted moving average (MEWMA) charts for m-attribute binomial processes; the attributes being the number of nonconforming items. Moreover, since the variable sample size and sampling interval (VSSI) MEWMA charts detect small process mean shifts faster than the traditional MEWMA, an economic design of the VSSI MEWMA chart is proposed to obtain the optimum design parameters of the chart. The sample size, the sampling interval, and the warning/action limit coefficients are obtained using a genetic algorithm such that the expected total cost per hour is minimized. At the end, a sensitivity analysis has been carried out to investigate the effects of the cost and the model parameters on the solution of the economic design of the VSSI MEWMA chart.     

A flexible generalization of the skew normal distribution based on a weighted normal distribution

The skew normal distribution of Azzalini (Scand J Stat 12:171–178, 1985) has been found suitable for unimodal density but with some skewness present. Through this article, we introduce a flexible extension of the Azzalini (Scand J Stat 12:171–178, 1985) skew normal distribution based on a symmetric component normal distribution (Gui et al. in J Stat Theory Appl 12(1):55–66, 2013). The proposed model can efficiently capture the bimodality, skewness and kurtosis criteria and heavy-tail property. The paper presents various basic properties of this family of distributions and provides two stochastic representations which are useful for obtaining theoretical properties and to simulate from the distribution. Further, maximum likelihood estimation of the parameters is studied numerically by simulation and the distribution is investigated by carrying out comparative fitting of three real datasets.     

A robust posterior preference multi-response optimization approach in multistage processes

Nowadays, many manufacturing and service systems provide products and services to their customers in several consecutive stages of operations, in each of which one or more quality characteristics of interest are monitored. In these environments, the final quality in the last stage not only depends on the quality of the task performed in that stage but also is dependent on the quality of the products and services in intermediate stages as well as the design parameters in each stage. In this paper, a novel methodology based on the posterior preference approach is proposed to robustly optimize these multistage processes. In this methodology, a multi-response surface optimization problem is solved in order to find preferred solutions among different non dominated solutions (NDSs) according to decision maker's preference. In addition, as the intermediate response variables (quality characteristics) may act as covariates in the next stages, a robust multi-response estimation method is applied to extract the relationships between the outputs and inputs of each stage. NDSs are generated by the ?-constraint method. The robust preferred solutions are selected considering some newly defined conformance criteria. The applicability of the proposed approach is illustrated by a numerical example at the end.     

Understanding the Role of Outsiders in Community-Based Development Interventions: A Framework with Findings from Iran

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - This article analyzes various roles of development practitioners (called outsiders) in five different cases of...     

Substance Use Disorders in a Sample of Iranian Secondary School Students

Aims: The current research assessed therate of substance use among Iranian secondaryschool students. Participants: 470boy secondary school students selected randomlyand were assessed. Measurements: Aconfidential questionnaire based on DSM-IV anda prior study was distributed, completed by thestudents and collected in the same sessions in2001. Results: Their mean age was13.6 yr., (SD = 0.67) ranging from 12 to 14. Ofthe subjects 105 (22.3%) reported usage ofsubstance (s) once or more sometime duringtheir lives: tobacco 82 (17.5%) alcohol 32(6.8%), opium 3 (–0.6%), hashish 1 (–0.2%), and heroin 2 (0.4%). Only 39 (8.3%) of the students were current substance dependent: 29 (6.2%) were tobacco dependent and 13 (2.8%)were alcohol dependent. Some were using morethan one substance. Conclusions:Tobacco and alcohol were found to be the mostprevalent form of substance use. Seekingpleasure, modeling and release of tension werethe most common reasons for initial substanceuse. The most common reasons reported forcurrent use were seeking pleasure, habit andrelease of tension, respectively. Thesefindings can be implicated in planningpreventive programs. Some of the findings aredifferent from those carried in the west.Cultural attitudes could be the causes of thedifferences.     

Iranian obstetricians’ views about the factors that influence pregnant women's choice of delivery method: A qualitative study

BackgroundCaesarean section is a commonly performed childbirth procedure. While caesarean section is undeniably a lifesaving procedure for some mothers and babies, it can also be an unnecessary procedure for others. Different factors may be involved in the rate of birth by caesarean section.QuestionThis qualitative study was conducted in Kashan city, Iran, to explore obstetrician's views of what might influence pregnant women's choice of delivery method.MethodsObstetricians’ views and experiences were collected using semi-structured interviews. Interviews were conducted in a clinic or hospital, based on the preference of the 18 physicians who agreed to participate in the study. All interviews were recorded and transcribed. Participants were asked key questions, including: “Why do some women prefer caesarean section?”; “What kind of delivery did you have and why?”; “What kind of delivery would you recommend to pregnant women and why?” Inductive qualitative content analysis was undertaken using the method described by Morreti with 120 initial codes categorized into six categories.FindingsThe six categories developed from the initial codes were: factors relating to women, obstetricians, delivery conditions, complications, society's beliefs and the health system.ConclusionA qualitative study conducted in Iran showed that most of the factors identified by participants facilitated the choice of caesarean section. Vaginal birth was anticipated as a painful and lengthy process, with low cultural acceptance and resulting in less income for obstetricians.     

Maximum likelihood estimation of sensitive proportion using repeated randomized response techniques

Randomized response techniques are designed to obtain usable data on sensitive issues while protecting the privacy of individuals. In this paper, based on repeating the randomized response technique, a new technique called repeated randomized response is introduced to increase the protection of privacy and efficiency of estimator for proportion of sensitive attribute. By using this technique, the proportion of academic cheating is estimated among students of Shahid Chamran University of Ahvaz, Ahvaz, Iran.     

Monitoring Multi-Attribute Processes Based on NORTA Inverse Transformed Vectors

Although multivariate statistical process control has been receiving a well-deserved attention in the literature, little work has been done to deal with multi-attribute processes. While by the NORTA algorithm one can generate an arbitrary multi-dimensional random vector by transforming a multi-dimensional standard normal vector, in this article, using inverse transformation method, we initially transform a multi-attribute random vector so that the marginal probability distributions associated with the transformed random variables are approximately normal. Then, we estimate the covariance matrix of the transformed vector via simulation. Finally, we apply the well-known T ² control chart to the transformed vector. We use some simulation experiments to illustrate the proposed method and to compare its performance with that of the deleted-Y method. The results show that the proposed method works better than the deleted-Y method in terms of the out-of-control average run length criterion.