Quantile Function for Rayleigh and Scaled Half Logistic: Application in Missing Data
Abstract
In this research paper quantile Functions for Scaled half logistic and Rayleigh distributions has been constructed. Data generated through the quantile Functions and then different limits for the full and missing data set have been developed with scale parameter. A number of such mean control limits could be constructed through purposed method but for analysis purpose few of them have discussed. The missing data limits broadened than the full data in each case, which was expected to be. The average run length (ARL) was also calculated for different sample sizes (50,100,150). The general decreasing behavior of ARL according to increasing shifts was observed that shows a worthy sign, for two distributions, as the probability of detecting an out of control signal increased due to decrease in ARL.
Downloads
References
2. Mccool, J.I. (2006). Control charts for radial error. Quality Technology and Quantitative Management, 3(3), 283-293.
3. Montgomery, D.C. (1999). Introduction to Statistical Quality Control. Wiley Series.
4. Nair, N.U. and Sankaran, P.G.(2009). Quantile-based reliability analysis. Communications in Statistics, 38, 222-232.
5. Nadarajah,S. and Kotz, S. (2006). The beta exponential distribution. Reliability engineering and system safety, 91(6),689-697
6. Naqvi, I.B., Aslam, M., and Aldosri, M.S.(2018). Weibull Quantile Function and Application in Missing Data. International Iournal of applied Mathematics and Statistics, 57(1), 65-72
7. Panichkitkosolkul, W. and Wattanachayakul, S. (2012). Bootstrap confidence intervals of the difference between two process capability indices for half Logistic distribution. Pakistan Journal of Statistics and Operational Research, 8(4), 879-894
8. Pearson, E.S. and Hartley, H.O. (1976). Tables for statisticians, Volume I, Biometrika Trust.
9. Rao, B.S. and Kantam, R.R.L. (2012). Mean and range charts for skewed distributions – A bomparison Based on half logistic distribution. Pakistan Journal of Statistics, 28(4), 437-444.
10. Rao, B.S., Nagendram, S. and Rosaiah, K. (2013). Exponential – Half Logistic Additive Failure Rate Model. International Journal of Scientific and Research Publications, 3(5), 1-10.
11. Schick, G.J. and Wolverton, R.W. (1973) Assessment of Software Reliability, in Proceedings of the Vortrage der jahrestagung 1972 dgor/papers of the annual meeting, pp. 395-422, Springer, New York, NY, USA.
12. Thomas, B., Nellikkattu, M. N. and Paduthol, S. G.(2014). A software reliability model using quantile function, Research Article. Journal of Probability and Statistics.
Copyright (c) 2019 Journal of Progressive Research in Mathematics
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.