Asymptotic and Bootstrap Confidence Intervals for the Process Capability Index cpy Based on Lindley Distributed Quality Characteristic

2019 ◽  
Vol 39 (1) ◽  
pp. 75-89 ◽  
Author(s):  
Mahendra Saha ◽  
Sumit Kumar ◽  
Sudhansu S. Maiti ◽  
Abhimanyu Singh Yadav ◽  
Sanku Dey
Keyword(s):  
Confidence Intervals ◽  
Process Capability ◽  
Process Capability Index ◽  
Quality Characteristic ◽  
Bootstrap Confidence Intervals ◽  
Capability Index

Bootstrap Confidence Intervals of the Modified Process Capability Index for Weibull distribution

2017 ◽  
Vol 42 (11) ◽  
pp. 4565-4573 ◽  
Author(s):  
Muhammad Kashif ◽  
Muhammad Aslam ◽  
G. Srinivasa Rao ◽  
Ali Hussein AL-Marshadi ◽  
Chi-Hyuck Jun
Keyword(s):  
Weibull Distribution ◽  
Confidence Intervals ◽  
Process Capability ◽  
Process Capability Index ◽  
Bootstrap Confidence Intervals ◽  
Capability Index

scholarly journals Evaluation of Bootstrap Confidence Intervals Using a New Non-Normal Process Capability Index

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 484 ◽  
Author(s):  
Gadde Srinivasa Rao ◽  
Mohammed Albassam ◽  
Muhammad Aslam
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Process Capability ◽  
Real Data ◽  
Process Capability Index ◽  
Monte Carlo Simulation Study ◽  
Bootstrap Confidence Intervals ◽  
Capability Index ◽  
Coverage Probabilities ◽  
Percentile Bootstrap

This paper assesses the bootstrap confidence intervals of a newly proposed process capability index (PCI) for Weibull distribution, using the logarithm of the analyzed data. These methods can be applied when the quality of interest has non-symmetrical distribution. Bootstrap confidence intervals, which consist of standard bootstrap (SB), percentile bootstrap (PB), and bias-corrected percentile bootstrap (BCPB) confidence interval are constructed for the proposed method. A Monte Carlo simulation study is used to determine the efficiency of newly proposed index Cpkw over the existing method by addressing the coverage probabilities and average widths. The outcome shows that the BCPB confidence interval is recommended. The methodology of the proposed index has been explained by using the real data of breaking stress of carbon fibers.

scholarly journals Evaluation of Modified Non-Normal Process Capability Index and Its Bootstrap Confidence Intervals

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 12135-12142 ◽  
Author(s):  
Muhammad Kashif ◽  
Muhammad Aslam ◽  
Ali Hussein Al-Marshadi ◽  
Chi-Hyuck Jun ◽  
Muhammad Imran Khan
Keyword(s):  
Confidence Intervals ◽  
Process Capability ◽  
Process Capability Index ◽  
Normal Process ◽  
Bootstrap Confidence Intervals ◽  
Capability Index

A New Method of Estimating the Process Capability Index with Exponential Distribution Using Interval Estimate of the Parameter

2019 ◽  
Vol 34 (2) ◽  
pp. 95-102
Author(s):  
Sai Sarada Vedururu ◽  
M. Subbarayudu ◽  
K. V. S. Sarma
Keyword(s):  
Exponential Distribution ◽  
Confidence Intervals ◽  
Weighted Average ◽  
Process Capability ◽  
Process Capability Index ◽  
Skewed Distribution ◽  
New Method ◽  
Quality Characteristic ◽  
Interval Estimate ◽  
Capability Index

Abstract This paper deals with a new method of deriving the Process Capability Index (PCI) when the quality characteristic X follows a positively skewed distribution. The focus of the paper is to derive a new estimate of PCI by taking into account the {100(1-{\alpha})}   Confidence Intervals (CI) of the parameter (s) and arriving at a new expression. The formula {{C}_{{s}}} , proposed by Wright (1995) which contains a component for skewness, is reexamined and a new estimate is constructed by utilizing the lower, middle and upper values of the CI of the parameter. The weighted average of the three possible estimates of {{C}_{{s}}} is proposed as the new estimate by taking the weights inversely proportional to the squared deviation from the hypothetical value of {{C}_{{s}}} . The properties of the estimate are studied by simulation using one parameter exponential distribution.

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