On designing new optimal synthetic Tukey’s control charts

Tukey’s control chart is generally used for monitoring the processes where the measurement process physically damages the product. It is based on single observation and robust to outliers. In this paper, two optimal synthetic Tukey’s control charts are proposed by integrating the conforming run length chart with the Tukey’s control chart and its modification. The performance comparison of the proposed charts with the existing Tukey’s control charts is made by using out-of-control average run length and extra quadratic loss as performance metrics. The proposed charts offer better protection against the process shifts as compare to the existing Tukey’s control charts when the underlying process distribution is symmetric or asymmetric. Simulation studies also establish the supremacy of the proposed control charts over the existing Tukey’s control charts. In the end, an illustrative example based on a real data set of the combined cycle power plant is provided for practical implementation.     

A power log-Dagum distribution: estimation and applications

Development and application of probability models in data analysis are of major importance for all sciences. Therefore, we introduce a new model called a power log-Dagum distribution defined on the entire real line. The model contains many new sub-models: power logistic, linear log-Dagum, linear logistic and log-Dagum distributions among them. Some properties of the model including three different estimation procedures are justified. The model exhibits various shapes for the density and hazard rate functions. Moreover, the estimation procedures are compared using simulation studies. Finally, the model with others are fitted to three data sets, and it shows a better fit than the compared distributions defined on the real line.     

Approximate Bayesian analysis of doubly censored samples from mixture of two Weibull distributions

The purpose of the paper is to estimate the parameters of the two-component mixture of Weibull distribution under doubly censored samples using Bayesian approach. The choice of Weibull distribution is made due to its (i) capability to model failure time data from engineering, medical and biological sciences (ii) added advantages over the well-known lifetime distributions such as exponential, Raleigh, lognormal and gamma distribution in terms of flexibility, increasing and decreasing hazard rate and closed-form distribution function and hazard rate. The proposed two-component mixture of Weibull distribution is even more flexible than its conventional form. However, the estimation of the parameters from the proposed mixture is more complex. Further, we have assumed couple of loss functions under non informative prior for the Bayesian analysis of the parameters from the mixture model. As the resultant Bayes estimators and associated posterior risks cannot be derived in the closed form, we have used the importance sampling and Lindley’s approximation to obtain the approximate estimates for the parameters of the mixture model. The comparison between the performances of approximation techniques has been made on the basis of simulation study and real-life data analysis. The importance sampling is found to be better than Lindley’s approximation as it gives better estimation for shape and mixing parameters of the mixture model and computations under this technique are much easier/shorter than those under Lindley’s approximation.     

The Performance of Gaussian and non Gaussian dynamic models in assessing market risk: The Implications for risk management

The developing markets are more volatile, unstable illiquid, and more prone to the external shocks. The non Gaussian VaR model gives more accurate risk models than Gaussian VaR models. Hence, the purpose of this study is to test if and how non Gaussian VaR models are comparatively better fit for risk modeling of developing markets than the Gaussian VaR models. The study measures the market risk for the daily closing price of Karachi Stock Exchange 100 index over the period of 2004–2016. The evaluation of VaR models suggests that non Gaussian dynamic model outperformed the Gaussian VaR models in developing markets.     

Does inflation bias stabilize real growth? Evidence from Pakistan

The motive of a typical discretionary central banker to accommodate excess inflation (inflation bias) is either to stabilize real growth or to spur it beyond natural rate. To what extent inflation bias helps to materialize this intention warrants empirical investigation. A more direct empirical probe into this issue, however, requires observable inflation bias indicators, which we model through desirable and threshold inflation rates as well as their respective society’s preferences. While examining the effects of inflation bias for a typical case of the discretionary monetary policy strategy of Pakistan, we found that contrary to the desired boost/stabilization in real growth, the policy (via. inflation bias) produced counterproductive results. Inflation bias was not merely ineffective in inducing real growth but significantly destabilized it. Moreover, the results, which are robust to different inflation bias indicators and subsample analysis, indicate that the higher the inflation bias, the higher is the intensity (magnitude) of its destabilizing effect and vice versa. This suggests that a policy that would minimize/constrain inflation bias would be a better choice as it would not only help achieve low and stable inflation but also a sustainable real economic growth.     

The Global Integration and Transmission of Social Values: A Case of Family Ties

Social Indicators Research - Family ties are arguably of particular importance in an era of globalization, where ideological and material changes in national and global arena intersect with...     

Dynamics of Corruption and Citizens’ Trust in Anti-Corruption Agencies in Three South Asian Countries

The comparative analyses of citizens’ trust in Anti-Corruption Agencies (ACAs) in three countries’ (Bangladesh, Nepal, and Sri Lanka) indicate that despite ACA’s low level of effectiveness in curbing corruption, trust level varies. The Nepalese ACA has higher level of trust than Bangladesh and Sri Lanka which is earned by targeting mainly lower level public officials. Though it fails to reduce the level of corruption in Nepal; citizens tend to trust ACA more. This is because of higher visibility of ACA’s activities. Such findings indicate about the methodological challenge to use ‘trust’ as a proxy to measure institutional performance.     

A new sampling plan under the exponential distribution

This article considers a sampling plan when the quality characteristic follows the exponential distribution. We provide the exact approach and propose an approximated approach. In the proposed approximation, a new statistic combined with a Weibull transformation is used for the normal approximation. The plan parameters are obtained through the two-point approach at the acceptable quality level (AQL) and the limiting quality level (LQL). The tables for plan parameters are reported according to various values of the AQL and the LQL when the producer's and the consumer's risks are given. A real example is given to illustrate the proposed approximation approach.     

Double moving average–EWMA control chart for exponentially distributed quality

A control chart is an ever-popular tool for monitoring the production process. The early detection of a process shift, if any, is the desire of the quality control personnel. In this article, an effective alternative control charting procedure has been developed for the monitoring of exponentially distributed quality characteristic using the double moving average combined with EWMA statistic. The performance of the proposed control chart is examined for different combinations of the shift constant, the EWMA smoothing parameter, the moving average span, and the target in-control average run lengths. It has been observed that the proposed control chart is more efficient in the detection of process shifts as compared to control chart suggested by Khoo and Wang for the same purpose. The proposed control chart is illustrated for practical usage with the help of a synthetic and a real dataset.