International Journal of Computer Applications Technology and Research, 2015
Road traffic accident in Nigeria is increasing at a worrying rate and has raised one of the count... more Road traffic accident in Nigeria is increasing at a worrying rate and has raised one of the country major concerns. We provided appropriate and suitable time series model for the consequences of road accident, the injured, killed and total casualty of the road accident in Nigeria. The most widely used conventional method, Autoregressive Integrated Moving Average (ARIMA) model of time series, also known as Box-Jenkins method is applied to yearly data on the consequences of road accident data in Nigeria from 1960-2013 to determine patterns of road traffic accident consequences; injured, killed and total casualty of the road accident along the Nigeria motorway. Appropriate models are developed for the accident consequences; injured, killed and total casualty. ARIMA (0; 2; 1) model is obtained for the injury and total casualty consequences, whilst ARIMA(1,2,2) model is obtained for the killed consequences, using the data from 1960-2011. The adequacy and the performance of the model are tested on the remaining data from 2012 to 2013. Seven years forecast are provided using the developed models and showed that road traffic accident consequences examined; injured, killed and total casualty would continue to increase on average.
Control charting techniques for monitoring the magnitude and frequency of an event are crucial in... more Control charting techniques for monitoring the magnitude and frequency of an event are crucial in manufacturing and industrial activities. Several articles on monitoring the two quantities separately are available in the literature, however, a simultaneous monitoring of the magnitude and frequency of an event may be more time efficient. In this paper, we propose the maximum exponentially weighted moving average (Max-EWMA) control chart to simultaneously monitor the magnitude and frequency of an event. The Max-EWMA chart's statistic is based on the maximum of the absolute values of two EWMA statistics-one for controlling the magnitude and the other for the frequency of an event. In addition, the magnitude is assumed to follow a gamma distribution while the frequency is assumed to follow an exponential distribution. The objective of the proposed scheme is to enhance the speed for detecting shifts in the magnitude and/or frequency of an event. Overall, performance study indicates t...
In this article, we propose an efficient control chart for monitoring small shifts in a process m... more In this article, we propose an efficient control chart for monitoring small shifts in a process mean for scenarios where the process variable is observed with a correlated auxiliary variable. The proposed chart, called an AHWMA chart, is an homogeneously weighted moving average type control chart that uses both the process and auxiliary variables in the form of a regression estimator to provide an efficient and unbiased estimate of the mean of the process variable. We provide the design structure of the chart and examine its performance in terms of its run length properties. Using a simulation study, we compare its run length performance with several existing methods for detecting a small shift in the process mean. Our simulation results show that the proposed chart is more efficient in detecting a small shift in the process mean than its competitors. We provide a detailed study of the chart's robustness to non-normal distributions and shown that the chart may also be designed to be less sensitive to non-normality. We give some recommendations on the application of the chart when the process parameters are unknown, and provide an example to show the implementation of the proposed new technique. INDEX TERMS Auxiliary variable; Average run length; control chart; parameter estimation; robustness.
International Journal of Computer Applications Technology and Research, 2015
Road traffic accident in Nigeria is increasing at a worrying rate and has raised one of the count... more Road traffic accident in Nigeria is increasing at a worrying rate and has raised one of the country major concerns. We provided appropriate and suitable time series model for the consequences of road accident, the injured, killed and total casualty of the road accident in Nigeria. The most widely used conventional method, Autoregressive Integrated Moving Average (ARIMA) model of time series, also known as Box-Jenkins method is applied to yearly data on the consequences of road accident data in Nigeria from 1960-2013 to determine patterns of road traffic accident consequences; injured, killed and total casualty of the road accident along the Nigeria motorway. Appropriate models are developed for the accident consequences; injured, killed and total casualty. ARIMA (0; 2; 1) model is obtained for the injury and total casualty consequences, whilst ARIMA(1,2,2) model is obtained for the killed consequences, using the data from 1960-2011. The adequacy and the performance of the model are tested on the remaining data from 2012 to 2013. Seven years forecast are provided using the developed models and showed that road traffic accident consequences examined; injured, killed and total casualty would continue to increase on average.
Control charting techniques for monitoring the magnitude and frequency of an event are crucial in... more Control charting techniques for monitoring the magnitude and frequency of an event are crucial in manufacturing and industrial activities. Several articles on monitoring the two quantities separately are available in the literature, however, a simultaneous monitoring of the magnitude and frequency of an event may be more time efficient. In this paper, we propose the maximum exponentially weighted moving average (Max-EWMA) control chart to simultaneously monitor the magnitude and frequency of an event. The Max-EWMA chart's statistic is based on the maximum of the absolute values of two EWMA statistics-one for controlling the magnitude and the other for the frequency of an event. In addition, the magnitude is assumed to follow a gamma distribution while the frequency is assumed to follow an exponential distribution. The objective of the proposed scheme is to enhance the speed for detecting shifts in the magnitude and/or frequency of an event. Overall, performance study indicates t...
In this article, we propose an efficient control chart for monitoring small shifts in a process m... more In this article, we propose an efficient control chart for monitoring small shifts in a process mean for scenarios where the process variable is observed with a correlated auxiliary variable. The proposed chart, called an AHWMA chart, is an homogeneously weighted moving average type control chart that uses both the process and auxiliary variables in the form of a regression estimator to provide an efficient and unbiased estimate of the mean of the process variable. We provide the design structure of the chart and examine its performance in terms of its run length properties. Using a simulation study, we compare its run length performance with several existing methods for detecting a small shift in the process mean. Our simulation results show that the proposed chart is more efficient in detecting a small shift in the process mean than its competitors. We provide a detailed study of the chart's robustness to non-normal distributions and shown that the chart may also be designed to be less sensitive to non-normality. We give some recommendations on the application of the chart when the process parameters are unknown, and provide an example to show the implementation of the proposed new technique. INDEX TERMS Auxiliary variable; Average run length; control chart; parameter estimation; robustness.
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