
doi: 10.3390/sym11040554
Variability or dispersion plays an important role in any process and provides insight into the spread of data from some central point, usually the mean. A process with less spread is preferred over a process in which values differ greatly from the mean. Various methods are available to estimate the process dispersion by using information on the variable of interest. Certain additional variables provide good insight to estimate the process dispersion. In this paper, we propose an efficient method for the estimation of process variability by using the exponential method. The properties of the proposed method were studied. We conducted simulation and empirical studies to compare the proposed method with some existing methods of estimation of variability. The results of the numerical study show that our proposed method is better than the other methods used in the study.
variability, transformation, mean square error, auxiliary variable
variability, transformation, mean square error, auxiliary variable
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 2 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
