Selection of Bayesian Single Sampling Plan with Zero – Inflated Poisson Distribution Based on Quality Region

M. Latha¹ , A. Palanisamy²

Section:Research Paper, Product Type: Isroset-Journal
Vol.5 , Issue.6 , pp.313-320, Dec-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v5i6.313320

Online published on Dec 31, 2018

Copyright © M. Latha, A. Palanisamy . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Acceptance sampling procedures are the practical tools for quality assurance application involving product control. Acceptance sampling systems are advocated when small sample size are necessary or desirable towards costlier testing for product quality. This paper is concerned with the set of tables for selection of Bayesian Single Sampling Plan with Zero – Inflated Poisson distribution on this basic of different combinations of entry parameter single sampling plan involving producer’s and consumer’s risks and probabilistic Quality Region, Indifference Quality Region for Specified AQL and LQL. Beta distributions are considered as prior distribution. Comparison is made with conventional Single Sampling Plan.

Key-Words / Index Term :
Bayesian Single Sampling Plan, Gamma Prior, Zero – Inflated Poisson distribution, Average Quality Limit (AQL), Limiting Quality Limit (LQL), Producer`s Risk (α),Consumer Risk (β), Indifference Quality Level (IQL), Probabilistic Quality Region (PQR), Indifference Quality Region (IQR)

References :
[1] D. Bohning, E Dietz, P. Schlattmann, “The zero-inflated Poisson model and the decayed, missing and filled teeth index in dental epidemiology”, Journal of Royal Statistical Society, Series A 162:195–209, (1999).
[2] T.W. Calvin, 1984. “How and when do perform Bayesian Acceptance Sampling”, American Society for Quality Vol.7, 1984.

[3] A. Hald, “Bayesian Single Sampling Plans for Discrete Prior Distribution”, Mat. Fys. Skr. Dan. Vid. Selsk., Vol.3, No.2, pp.88, (1965). Copenhagen, Munksgaard.
[4] D. Lambert, “Zero-inflated Poisson regression with an application to defects in manufacturing”, Technimetrics 34:1–14, (1992)
[5] A .Loganathan, and K. Shalini, “Determination of Single sampling plans by attributes under the conditions of Zero – inflated Poisson distribution”, Communications in Statistics – Simulation and Computation 43:3, 538-548, ,(2013)
[6] M. Latha, and K. Subbiah, “Selection of Bayesian multiple deferred state (BMDS-1) sampling plan based on quality regions”, Internat. J. Recent Scientific Res., 6 (4): 275-282, (2015).
[7] G. McLachlan, D. Peel, “Finite Mixture Models”, New York: John Wiley & Sons. (2000).
[8] H. Naya, J.I Urioste, Y.M Chang, M.R Motta, R. Kremer, D. Gianola,“A comparison between Poisson and zero-inflated Poisson regression models with an application to number of black spots in corriedale sheep”, Genetics Selection Evolution 40:379–394, (2008).
[9] H. Sim, M.H Lim, “Attribute charts for zero-inflated processes”, Communications in Statistics- Simulation and Computation 37:1440–1452, (2008).
[10] K.S.Stephens, “The Handbook of Applied Acceptance Sampling Plans, Procedures and Principles. Wisconsin”. ASQ Quality Press. (2001).
[11] K.K. Suresh and M. Latha , “Bayesian Single Sampling Plan for a Gamma Prior” Economic Quality Control, Vol.16, No.l, pp.93-107. (2001),
[12] K.K. Suresh and M. Latha (2002), “Selection of Bayesian Single Sampling Plan with Gamma Prior Distribution”, Statistical Methods, Vol.4, No.l, pp.64-74, (2002)
[13] J. Yang, M. Xie, T.N. Goh, “Outlier identification and robust parameter estimation in a zero-inflated Poisson model”, Journal of Applied Statistics 38:421–430, (2011).