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This study introduces a new qualitative randomized response technique for gathering trust-worthy sensitive data using a perfect (imperfect) ranked set sampling design. This model takes into account the sizes (weights) of various balls on the randomizing device to choose one of the two questions (out of which one is sensitive); so the respondent feels, liberty in choosing balls. In this way, the likelihood of an honest response increases. It is established that the procedure is easy to apply and the estimate under the proposed model is more precise than the original Warner's randomized response model. In addition, an application to real medical data is considered. Finally, a cost analysis of the proposed model is also presented. [ABSTRACT FROM AUTHOR]

Some memory-based control charts such as cumulative sum (CUSUM) control chart, exponentially weighted moving average (EWMA) control chart, and homogeneously weighted moving average (HWMA) control chart are used to monitor small-to-moderate shift in the process mean. An HWMA control chart is a memory-based enhanced version of an EWMA control chart because it detects smaller shifts earlier than an ordinary EWMA control chart. The use of auxiliary information-based HWMA statistic enhances the efficiency of a usual CUSUM control chart. This study intends to present an efficient version of usual CUSUM control chart by utilizing an auxiliary information-based HWMA statistic as input information into usual CUSUM control chart. Supremacy of the proposed control chart is established on the basis of average run length (ARL), percent decrease in ARL (PDARL), and Friedman rank test (FRT) by conducting a Monte Carlo simulation. A real-life application shows the implementation of the proposed control chart. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19) pandemic has recently become a global health crisis. On the basis of this study the data reported from ten different countries on confirmed daily deaths caused by COVID-19. By fitting the linear regression models based on the data from ten countries to find the relationship between the new cases and deaths reported daily. We also used the autoregressive integrated moving average model (ARIMA) to predict the potential number of daily deaths caused by COVID-19 in these countries in the next 3 Months. The R2value obtained for Iran (0.24) implies that 24% of daily deaths correspond to the daily cases. The R2of Pakistan 0.662 which indicates that 66.2% of daily deaths are explained by our predictor variable. In Turkey 70.2% of daily deaths are explained by daily cases and India recorded the highest number of deaths while UAE had the lowest number of deaths. Our results suggest that the pandemic is under control in China, UAE and Australia. Pakistan, Iran, Germany and Italy however, showed an upward trend in the spread of the disease, which may correlate with a high increase in death rate as the data indicated. [ABSTRACT FROM AUTHOR]

This paper considers a class of generalized estimators for estimating the unknown population variance using two auxiliary variables when the mean of one auxiliary variable may not be available. The expressions for bias and mean square error of the proposed estimators are obtained up to the first order of approximation. Conditions for which the proposed generalized estimator is more efficient than the existing estimators have been derived. Both empirical and simulation studies have also been carried out to analyze the efficiency of the proposed estimators with some existing estimators. [ABSTRACT FROM AUTHOR]