Assessing the process capability index %$S_\mathrm{{pmk}}%$ using improved estimators
Abstract Process capability indices (PCIs) are widely used to determine whether the production process is working according to given specifications or not. It plays an important role in monitoring and analyzing process quality and productivity. Since PCI is based on sample observations, it is a poin...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Saha, Mahendra [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
Asymptotic confidence interval |
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| Anmerkung: |
© Society for Reliability and Safety (SRESA) 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Life cycle reliability and safety engineering - [Singapore] : Springer Singapore, 2017, 8(2019), 3 vom: 03. Juni, Seite 211-218 |
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| Übergeordnetes Werk: |
volume:8 ; year:2019 ; number:3 ; day:03 ; month:06 ; pages:211-218 |
| Links: |
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| DOI / URN: |
10.1007/s41872-019-00081-4 |
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| Katalog-ID: |
SPR038327007 |
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| 245 | 1 | 0 | |a Assessing the process capability index %$S_\mathrm{{pmk}}%$ using improved estimators |
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| 520 | |a Abstract Process capability indices (PCIs) are widely used to determine whether the production process is working according to given specifications or not. It plays an important role in monitoring and analyzing process quality and productivity. Since PCI is based on sample observations, it is a point estimate of the true PCI. It is well known that confidence interval (CI) provides much more information about the population characteristic of interest than a point estimate. In this paper, new estimator for PCI %$S_\mathrm{{pmk}}%$ has been proposed using improved estimators of population mean and variance. The proposed and classical index is compared with respect to mean squared error (MSE). Numerical results are provided to illustrate the proposed index and to judge the merits of the proposed estimators. Further, asymptotic confidence interval (ACI) and three non-parametric BCIs, namely, standard bootstrap (s-boot), percentile bootstrap (p-boot) and bias-corrected percentile bootstrap (BCp-boot) of the proposed modified PCI %$S_\mathrm{{pmk}}%$ are studied through simulation when the underlying distribution follows Lindley distribution. Method of maximum likelihood is used to estimate the parameter of the model. A Monte Carlo simulation has been used to investigate the estimated coverage probabilities and average widths of the ACI and BCIs for the modified PCI %$S_\mathrm{{pmk}}%$. Finally, four real data sets are analyzed for illustrative purposes. | ||
| 650 | 4 | |a Process capability index |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Maximum likelihood estimate |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Asymptotic confidence interval |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Bootstrap confidence intervals |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Lindley distribution |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Dey, Sanku |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Life cycle reliability and safety engineering |d [Singapore] : Springer Singapore, 2017 |g 8(2019), 3 vom: 03. Juni, Seite 211-218 |w (DE-627)887305059 |w (DE-600)2894228-0 |x 2520-1360 |7 nnns |
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10.1007/s41872-019-00081-4 doi (DE-627)SPR038327007 (SPR)s41872-019-00081-4-e DE-627 ger DE-627 rakwb eng Saha, Mahendra verfasserin (orcid)0000-0002-0819-5696 aut Assessing the process capability index %$S_\mathrm{{pmk}}%$ using improved estimators 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Society for Reliability and Safety (SRESA) 2019 Abstract Process capability indices (PCIs) are widely used to determine whether the production process is working according to given specifications or not. It plays an important role in monitoring and analyzing process quality and productivity. Since PCI is based on sample observations, it is a point estimate of the true PCI. It is well known that confidence interval (CI) provides much more information about the population characteristic of interest than a point estimate. In this paper, new estimator for PCI %$S_\mathrm{{pmk}}%$ has been proposed using improved estimators of population mean and variance. The proposed and classical index is compared with respect to mean squared error (MSE). Numerical results are provided to illustrate the proposed index and to judge the merits of the proposed estimators. Further, asymptotic confidence interval (ACI) and three non-parametric BCIs, namely, standard bootstrap (s-boot), percentile bootstrap (p-boot) and bias-corrected percentile bootstrap (BCp-boot) of the proposed modified PCI %$S_\mathrm{{pmk}}%$ are studied through simulation when the underlying distribution follows Lindley distribution. Method of maximum likelihood is used to estimate the parameter of the model. A Monte Carlo simulation has been used to investigate the estimated coverage probabilities and average widths of the ACI and BCIs for the modified PCI %$S_\mathrm{{pmk}}%$. Finally, four real data sets are analyzed for illustrative purposes. Process capability index (dpeaa)DE-He213 Maximum likelihood estimate (dpeaa)DE-He213 Asymptotic confidence interval (dpeaa)DE-He213 Bootstrap confidence intervals (dpeaa)DE-He213 Lindley distribution (dpeaa)DE-He213 Dey, Sanku aut Enthalten in Life cycle reliability and safety engineering [Singapore] : Springer Singapore, 2017 8(2019), 3 vom: 03. Juni, Seite 211-218 (DE-627)887305059 (DE-600)2894228-0 2520-1360 nnns volume:8 year:2019 number:3 day:03 month:06 pages:211-218 https://dx.doi.org/10.1007/s41872-019-00081-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2019 3 03 06 211-218 |
| spelling |
10.1007/s41872-019-00081-4 doi (DE-627)SPR038327007 (SPR)s41872-019-00081-4-e DE-627 ger DE-627 rakwb eng Saha, Mahendra verfasserin (orcid)0000-0002-0819-5696 aut Assessing the process capability index %$S_\mathrm{{pmk}}%$ using improved estimators 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Society for Reliability and Safety (SRESA) 2019 Abstract Process capability indices (PCIs) are widely used to determine whether the production process is working according to given specifications or not. It plays an important role in monitoring and analyzing process quality and productivity. Since PCI is based on sample observations, it is a point estimate of the true PCI. It is well known that confidence interval (CI) provides much more information about the population characteristic of interest than a point estimate. In this paper, new estimator for PCI %$S_\mathrm{{pmk}}%$ has been proposed using improved estimators of population mean and variance. The proposed and classical index is compared with respect to mean squared error (MSE). Numerical results are provided to illustrate the proposed index and to judge the merits of the proposed estimators. Further, asymptotic confidence interval (ACI) and three non-parametric BCIs, namely, standard bootstrap (s-boot), percentile bootstrap (p-boot) and bias-corrected percentile bootstrap (BCp-boot) of the proposed modified PCI %$S_\mathrm{{pmk}}%$ are studied through simulation when the underlying distribution follows Lindley distribution. Method of maximum likelihood is used to estimate the parameter of the model. A Monte Carlo simulation has been used to investigate the estimated coverage probabilities and average widths of the ACI and BCIs for the modified PCI %$S_\mathrm{{pmk}}%$. Finally, four real data sets are analyzed for illustrative purposes. Process capability index (dpeaa)DE-He213 Maximum likelihood estimate (dpeaa)DE-He213 Asymptotic confidence interval (dpeaa)DE-He213 Bootstrap confidence intervals (dpeaa)DE-He213 Lindley distribution (dpeaa)DE-He213 Dey, Sanku aut Enthalten in Life cycle reliability and safety engineering [Singapore] : Springer Singapore, 2017 8(2019), 3 vom: 03. Juni, Seite 211-218 (DE-627)887305059 (DE-600)2894228-0 2520-1360 nnns volume:8 year:2019 number:3 day:03 month:06 pages:211-218 https://dx.doi.org/10.1007/s41872-019-00081-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2019 3 03 06 211-218 |
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10.1007/s41872-019-00081-4 doi (DE-627)SPR038327007 (SPR)s41872-019-00081-4-e DE-627 ger DE-627 rakwb eng Saha, Mahendra verfasserin (orcid)0000-0002-0819-5696 aut Assessing the process capability index %$S_\mathrm{{pmk}}%$ using improved estimators 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Society for Reliability and Safety (SRESA) 2019 Abstract Process capability indices (PCIs) are widely used to determine whether the production process is working according to given specifications or not. It plays an important role in monitoring and analyzing process quality and productivity. Since PCI is based on sample observations, it is a point estimate of the true PCI. It is well known that confidence interval (CI) provides much more information about the population characteristic of interest than a point estimate. In this paper, new estimator for PCI %$S_\mathrm{{pmk}}%$ has been proposed using improved estimators of population mean and variance. The proposed and classical index is compared with respect to mean squared error (MSE). Numerical results are provided to illustrate the proposed index and to judge the merits of the proposed estimators. Further, asymptotic confidence interval (ACI) and three non-parametric BCIs, namely, standard bootstrap (s-boot), percentile bootstrap (p-boot) and bias-corrected percentile bootstrap (BCp-boot) of the proposed modified PCI %$S_\mathrm{{pmk}}%$ are studied through simulation when the underlying distribution follows Lindley distribution. Method of maximum likelihood is used to estimate the parameter of the model. A Monte Carlo simulation has been used to investigate the estimated coverage probabilities and average widths of the ACI and BCIs for the modified PCI %$S_\mathrm{{pmk}}%$. Finally, four real data sets are analyzed for illustrative purposes. Process capability index (dpeaa)DE-He213 Maximum likelihood estimate (dpeaa)DE-He213 Asymptotic confidence interval (dpeaa)DE-He213 Bootstrap confidence intervals (dpeaa)DE-He213 Lindley distribution (dpeaa)DE-He213 Dey, Sanku aut Enthalten in Life cycle reliability and safety engineering [Singapore] : Springer Singapore, 2017 8(2019), 3 vom: 03. Juni, Seite 211-218 (DE-627)887305059 (DE-600)2894228-0 2520-1360 nnns volume:8 year:2019 number:3 day:03 month:06 pages:211-218 https://dx.doi.org/10.1007/s41872-019-00081-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2019 3 03 06 211-218 |
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10.1007/s41872-019-00081-4 doi (DE-627)SPR038327007 (SPR)s41872-019-00081-4-e DE-627 ger DE-627 rakwb eng Saha, Mahendra verfasserin (orcid)0000-0002-0819-5696 aut Assessing the process capability index %$S_\mathrm{{pmk}}%$ using improved estimators 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Society for Reliability and Safety (SRESA) 2019 Abstract Process capability indices (PCIs) are widely used to determine whether the production process is working according to given specifications or not. It plays an important role in monitoring and analyzing process quality and productivity. Since PCI is based on sample observations, it is a point estimate of the true PCI. It is well known that confidence interval (CI) provides much more information about the population characteristic of interest than a point estimate. In this paper, new estimator for PCI %$S_\mathrm{{pmk}}%$ has been proposed using improved estimators of population mean and variance. The proposed and classical index is compared with respect to mean squared error (MSE). Numerical results are provided to illustrate the proposed index and to judge the merits of the proposed estimators. Further, asymptotic confidence interval (ACI) and three non-parametric BCIs, namely, standard bootstrap (s-boot), percentile bootstrap (p-boot) and bias-corrected percentile bootstrap (BCp-boot) of the proposed modified PCI %$S_\mathrm{{pmk}}%$ are studied through simulation when the underlying distribution follows Lindley distribution. Method of maximum likelihood is used to estimate the parameter of the model. A Monte Carlo simulation has been used to investigate the estimated coverage probabilities and average widths of the ACI and BCIs for the modified PCI %$S_\mathrm{{pmk}}%$. Finally, four real data sets are analyzed for illustrative purposes. Process capability index (dpeaa)DE-He213 Maximum likelihood estimate (dpeaa)DE-He213 Asymptotic confidence interval (dpeaa)DE-He213 Bootstrap confidence intervals (dpeaa)DE-He213 Lindley distribution (dpeaa)DE-He213 Dey, Sanku aut Enthalten in Life cycle reliability and safety engineering [Singapore] : Springer Singapore, 2017 8(2019), 3 vom: 03. Juni, Seite 211-218 (DE-627)887305059 (DE-600)2894228-0 2520-1360 nnns volume:8 year:2019 number:3 day:03 month:06 pages:211-218 https://dx.doi.org/10.1007/s41872-019-00081-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2019 3 03 06 211-218 |
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10.1007/s41872-019-00081-4 doi (DE-627)SPR038327007 (SPR)s41872-019-00081-4-e DE-627 ger DE-627 rakwb eng Saha, Mahendra verfasserin (orcid)0000-0002-0819-5696 aut Assessing the process capability index %$S_\mathrm{{pmk}}%$ using improved estimators 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Society for Reliability and Safety (SRESA) 2019 Abstract Process capability indices (PCIs) are widely used to determine whether the production process is working according to given specifications or not. It plays an important role in monitoring and analyzing process quality and productivity. Since PCI is based on sample observations, it is a point estimate of the true PCI. It is well known that confidence interval (CI) provides much more information about the population characteristic of interest than a point estimate. In this paper, new estimator for PCI %$S_\mathrm{{pmk}}%$ has been proposed using improved estimators of population mean and variance. The proposed and classical index is compared with respect to mean squared error (MSE). Numerical results are provided to illustrate the proposed index and to judge the merits of the proposed estimators. Further, asymptotic confidence interval (ACI) and three non-parametric BCIs, namely, standard bootstrap (s-boot), percentile bootstrap (p-boot) and bias-corrected percentile bootstrap (BCp-boot) of the proposed modified PCI %$S_\mathrm{{pmk}}%$ are studied through simulation when the underlying distribution follows Lindley distribution. Method of maximum likelihood is used to estimate the parameter of the model. A Monte Carlo simulation has been used to investigate the estimated coverage probabilities and average widths of the ACI and BCIs for the modified PCI %$S_\mathrm{{pmk}}%$. Finally, four real data sets are analyzed for illustrative purposes. Process capability index (dpeaa)DE-He213 Maximum likelihood estimate (dpeaa)DE-He213 Asymptotic confidence interval (dpeaa)DE-He213 Bootstrap confidence intervals (dpeaa)DE-He213 Lindley distribution (dpeaa)DE-He213 Dey, Sanku aut Enthalten in Life cycle reliability and safety engineering [Singapore] : Springer Singapore, 2017 8(2019), 3 vom: 03. Juni, Seite 211-218 (DE-627)887305059 (DE-600)2894228-0 2520-1360 nnns volume:8 year:2019 number:3 day:03 month:06 pages:211-218 https://dx.doi.org/10.1007/s41872-019-00081-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2019 3 03 06 211-218 |
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Enthalten in Life cycle reliability and safety engineering 8(2019), 3 vom: 03. Juni, Seite 211-218 volume:8 year:2019 number:3 day:03 month:06 pages:211-218 |
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Saha, Mahendra @@aut@@ Dey, Sanku @@aut@@ |
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| author |
Saha, Mahendra |
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Saha, Mahendra misc Process capability index misc Maximum likelihood estimate misc Asymptotic confidence interval misc Bootstrap confidence intervals misc Lindley distribution Assessing the process capability index %$S_\mathrm{{pmk}}%$ using improved estimators |
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Assessing the process capability index %$S_\mathrm{{pmk}}%$ using improved estimators Process capability index (dpeaa)DE-He213 Maximum likelihood estimate (dpeaa)DE-He213 Asymptotic confidence interval (dpeaa)DE-He213 Bootstrap confidence intervals (dpeaa)DE-He213 Lindley distribution (dpeaa)DE-He213 |
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assessing the process capability index %$s_\mathrm{{pmk}}%$ using improved estimators |
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Assessing the process capability index %$S_\mathrm{{pmk}}%$ using improved estimators |
| abstract |
Abstract Process capability indices (PCIs) are widely used to determine whether the production process is working according to given specifications or not. It plays an important role in monitoring and analyzing process quality and productivity. Since PCI is based on sample observations, it is a point estimate of the true PCI. It is well known that confidence interval (CI) provides much more information about the population characteristic of interest than a point estimate. In this paper, new estimator for PCI %$S_\mathrm{{pmk}}%$ has been proposed using improved estimators of population mean and variance. The proposed and classical index is compared with respect to mean squared error (MSE). Numerical results are provided to illustrate the proposed index and to judge the merits of the proposed estimators. Further, asymptotic confidence interval (ACI) and three non-parametric BCIs, namely, standard bootstrap (s-boot), percentile bootstrap (p-boot) and bias-corrected percentile bootstrap (BCp-boot) of the proposed modified PCI %$S_\mathrm{{pmk}}%$ are studied through simulation when the underlying distribution follows Lindley distribution. Method of maximum likelihood is used to estimate the parameter of the model. A Monte Carlo simulation has been used to investigate the estimated coverage probabilities and average widths of the ACI and BCIs for the modified PCI %$S_\mathrm{{pmk}}%$. Finally, four real data sets are analyzed for illustrative purposes. © Society for Reliability and Safety (SRESA) 2019 |
| abstractGer |
Abstract Process capability indices (PCIs) are widely used to determine whether the production process is working according to given specifications or not. It plays an important role in monitoring and analyzing process quality and productivity. Since PCI is based on sample observations, it is a point estimate of the true PCI. It is well known that confidence interval (CI) provides much more information about the population characteristic of interest than a point estimate. In this paper, new estimator for PCI %$S_\mathrm{{pmk}}%$ has been proposed using improved estimators of population mean and variance. The proposed and classical index is compared with respect to mean squared error (MSE). Numerical results are provided to illustrate the proposed index and to judge the merits of the proposed estimators. Further, asymptotic confidence interval (ACI) and three non-parametric BCIs, namely, standard bootstrap (s-boot), percentile bootstrap (p-boot) and bias-corrected percentile bootstrap (BCp-boot) of the proposed modified PCI %$S_\mathrm{{pmk}}%$ are studied through simulation when the underlying distribution follows Lindley distribution. Method of maximum likelihood is used to estimate the parameter of the model. A Monte Carlo simulation has been used to investigate the estimated coverage probabilities and average widths of the ACI and BCIs for the modified PCI %$S_\mathrm{{pmk}}%$. Finally, four real data sets are analyzed for illustrative purposes. © Society for Reliability and Safety (SRESA) 2019 |
| abstract_unstemmed |
Abstract Process capability indices (PCIs) are widely used to determine whether the production process is working according to given specifications or not. It plays an important role in monitoring and analyzing process quality and productivity. Since PCI is based on sample observations, it is a point estimate of the true PCI. It is well known that confidence interval (CI) provides much more information about the population characteristic of interest than a point estimate. In this paper, new estimator for PCI %$S_\mathrm{{pmk}}%$ has been proposed using improved estimators of population mean and variance. The proposed and classical index is compared with respect to mean squared error (MSE). Numerical results are provided to illustrate the proposed index and to judge the merits of the proposed estimators. Further, asymptotic confidence interval (ACI) and three non-parametric BCIs, namely, standard bootstrap (s-boot), percentile bootstrap (p-boot) and bias-corrected percentile bootstrap (BCp-boot) of the proposed modified PCI %$S_\mathrm{{pmk}}%$ are studied through simulation when the underlying distribution follows Lindley distribution. Method of maximum likelihood is used to estimate the parameter of the model. A Monte Carlo simulation has been used to investigate the estimated coverage probabilities and average widths of the ACI and BCIs for the modified PCI %$S_\mathrm{{pmk}}%$. Finally, four real data sets are analyzed for illustrative purposes. © Society for Reliability and Safety (SRESA) 2019 |
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Assessing the process capability index %$S_\mathrm{{pmk}}%$ using improved estimators |
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https://dx.doi.org/10.1007/s41872-019-00081-4 |
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Dey, Sanku |
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