Solving Noisy ICA Using Multivariate Wavelet Denoising with an Application to Noisy Latent Variables Regression

A novel approach to solve the independent component analysis (ICA) model in the presence of noise is proposed. We use wavelets as natural denoising tools to solve the noisy ICA model. To do this, we use a multivariate wavelet denoising algorithm allowing spatial and temporal dependency. We propose also using a statistical approach, named nested design of experiments, to select the parameters such as wavelet family and thresholding type. This technique helps us to select more convenient combination of the parameters. This approach could be extended to many other problems in which one needs to choose parameters between many choices. The performance of the proposed method is illustrated on the simulated data and promising results are obtained. Also, the suggested method applied in latent variables regression in the presence of noise on real data. The good results confirm the ability of multivariate wavelet denoising to solving noisy ICA.     

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.     

New Statistic to Increase Correctness in Simulation Factor Screening Using Frequency Domain Method

Simulation models often include a large number of input factors, many of them may be unimportant to the output; justifying the use of factor screening experiments to eliminate unimportant input factors from consideration in later stages of analysis. With a large number of factors, the challenge is designing experiments so that total number of runs and consequently the required time and cost decrease while achieving a satisfactory detection rate. This article employs frequency domain method (FDM) which is applicable in discrete-event simulation models to propose a new statistic defined as the ratio of estimated signal spectrum to maximum estimated noise spectrum. The proposed method not only has the FDM advantages compared to classic screening approaches but also helps to reduce the error of associated with distinguishing important effects from unimportant ones. Furthermore, as an alternative to the existing statistics, it is shown that not only the proposed statistic does not deteriorate the power of the screening test but in some instances it helps to improve it.     

Managing White‐Collar Work: An Operations‐Oriented Survey

Although white‐collar work is of vast importance to the economy, the operations management (OM) literature has focused largely on traditional blue‐collar work. In an effort to stimulate more OM research into the design, control, and management of white‐collar work systems, this paper provides a systematic review of disparate streams of research relevant to understanding white‐collar work from an operations perspective. Our review classifies research according to its relevance to white‐collar work at individual, team, and organizational levels. By examining the literature in the context of this framework, we identify gaps in our understanding of white‐collar work that suggest promising research directions.     

An Integrated Methodology for Assessment and Selection of the Project Risk Response Actions

In a systematic process of project risk management, after risk assessment is implemented, the risk analysts encounter the phase of assessment and selection of the project risk response actions (RA). As indicated by many researchers, there are less systematic and well-developed solutions in the area of risk response assessment and selection. The present article introduces a methodology including a modeling approach with the objective of selecting a set of RA that minimizes the undesirable deviation from achieving the project scope. The developed objective function comprises the three key success criteria of a project, namely, time, quality, and cost. Our model integrates overall project management into the project risk response planning (P2RP). Furthermore, the proposed model stresses on an equivalent importance for both "risk" and "response." We believe that applying the proposed model helps the project risk analyst in most effective and efficient manner dealing with his or her complicated RA selection problems. The application of the proposed model was implemented in projects in the construction industry in which it showed tremendous time, cost, and quality improvements.     

Women’s Well-Being in Iran: Territorial Analysis Using a Multidimensional Approach

This paper employs a multidimensional index for assessing women’s well-being in Iranian provinces using demographic and health survey macro data. Besides economic status as a traditional dimension for objective well-being, other factors such as health, education, risk, and technology as well as self-reported life satisfaction and happiness (psychological well-being) have been included as non-material well-being dimensions. We found that the values of women’s well-being are distributed unequally across the provinces. Southern provinces demonstrate the worst performances and central provinces have the best. Generally, Iranian women are in poor condition in terms of economic variables (e.g. income and participation in the job market), psychological well-being (life satisfaction), HIV literacy, use of the internet, and access to mass media. On the other hand, in recent years, they have made significant progress in education (such as the rate of literacy, high school degree), and health (safe pregnancy care and nutritional supplements).     

Comparison of estimation methods for the Weibull distribution

Weibull distributions have received wide ranging applications in many areas including reliability, hydrology and communication systems. Many estimation methods have been proposed for Weibull distributions. But there has not been a comprehensive comparison of these estimation methods. Most studies have focused on comparing the maximum likelihood estimation (MLE) with one of the other approaches. In this paper, we first propose an L-moment estimator for the Weibull distribution. Then, a comprehensive comparison is made of the following methods: the method of maximum likelihood estimation (MLE), the method of logarithmic moments, the percentile method, the method of moments and the method of L-moments.     

Cyber risk assessment in small and medium-sized enterprises: A multilevel decision-making approach for small e-tailors

The role played by information and communication technologies in today's businesses cannot be underestimated. While such technological advancements provide numerous advantages and opportunities, they are known to thread organizations with new challenges such as cyberattacks. This is particularly important for small and medium-sized enterprises (SMEs) that are deemed to be the least mature and highly vulnerable to cybersecurity risks. Thus, this research is set to assess the cyber risks in online retailing SMEs (e-tailing SMEs). Therefore, this article employs a sample of 124 small e-tailers in the United Kingdom and takes advantage of a multi-criteria decision analysis (MCDA) method. Indeed, we identified a total number of 28 identified cyber-oriented risks in five exhaustive themes of “security,” “dependency,” “employee,” “strategic,” and “legal” risks. Subsequently, an integrated approach using step-wise weight assessment ratio analysis (SWARA) and best–worst method (BWM) has been employed to develop a pathway of risk assessment. As such, the current study outlines a novel approach toward cybersecurity risk management for e-tailing SMEs and discusses its effectiveness and contributions to the cyber risk management literature.     

Optimal Production and Admission Policies in Make‐to‐Stock/Make‐to‐Order Manufacturing Systems

In this article, we study optimal production and admission control policies in manufacturing systems that produce two types of products: one type consists of identical items that are produced to stock, while the other has varying features and is produced to order. The model is motivated by applications from various industries, in particular, the automobile industry, where a part supplier receives orders from both an original equipment manufacturer and the aftermarket. The product for the original equipment manufacturer is produced to stock, it has higher priority, and its demands are fully accepted. The aftermarket product is produced to order, and its demands can be either accepted or rejected. We characterize the optimal production and admission policies with a partial‐linear structure, and using computational analysis, we provide insights into the benefits of the new policies. We also investigate the impact of production capacity, cost structure, and demand structure on system performance.     

Bivariate negative binomial regression model with excess zeros and right censoring: an application to Indonesian data

We propose a bivariate hurdle negative binomial (BHNB) regression model with right censoring to model correlated bivariate count data with excess zeros and few extreme observations. The parameters of the BHNB regression model are obtained using maximum likelihood with conjugate gradient optimization. The proposed model is applied to actual survey data where the bivariate outcome is number of days missed from primary activities and number of days spent in bed due to illness during the 4-week period preceding the inquiry date. We compared the right censored BHNB model to the right censored bivariate negative binomial (BNB) model. A simulation study is conducted to discuss some properties of the BHNB model. Our proposed model demonstrated superior performance in goodness-of-fit of estimated frequencies.KEYWORDS: Zero inflation, over-dispersion, parameter estimation, model selection, right censoring