Quantile Function for Rayleigh and Scaled Half Logistic: Application in Missing Data
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.
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.
TRANSFER OF COPYRIGHT
JPRM is pleased to undertake the publication of your contribution to Journal of Progressive Research in Mathematics.
The copyright to this article is transferred to JPRM(including without limitation, the right to publish the work in whole or in part in any and all forms of media, now or hereafter known) effective if and when the article is accepted for publication thus granting JPRM all rights for the work so that both parties may be protected from the consequences of unauthorized use.