Research Article
BibTex RIS Cite
Year 2023, Volume: 36 Issue: 1, 284 - 299, 01.03.2023
https://doi.org/10.35378/gujs.916270

Abstract

References

  • [1] Weibull, W., “A statistical theory of the strength of materials”, Ingvetenskaps Akademiens Handlingar, 151: 1-45, (1939).
  • [2] Barbero, E., Fernández-Sáez, J., Navarro, C., “Statistical analysis of the mechanical properties of composite materials”, Composites Part B: Engineering, 31(5): 375-381, (2000).
  • [3] McCool, J., “Flexural strength tests of brittle materials: Selecting the number of specimens and determining confidence limits for Weibull parameters”, Journal of Testing and Evaluation, 45(2): 664-670, (2016).
  • [4] Barbero, E., Fernández-Sáez, J., Navarro, C., “Statistical distribution of the estimator of Weibull modulus”, Journal of Materials Science Letters, 20(9): 847-849, (2001).
  • [5] McCool, J.I. “Using the Weibull distribution: Reliability, Modeling, and Inference”, 1st ed, NJ: John Wiley & Sons Inc, 97-126, (2012).
  • [6] Casella, G., Berger, R.L. “Statistical Inference”, 2nd ed, USA: Thomson Learning, 417-449, (2002).
  • [7] Guenther, W.C., “Shortest confidence intervals”, The American Statistician, 23: 22-25, (1969).
  • [8] Guenther, W.C., “Unbiased confidence intervals”, The American Statistician, 25: 51-53, (1971).
  • [9] McCool, J.I., “Inference on Weibull percentiles and shape parameter from maximum likelihood estimates”, IEEE Transactions on Reliability, R-19: 2–9, (1970).
  • [10] Thoman, D.R., Bain L.J., Antle C.E., “Inferences on the parameters of the Weibull distribution”, Technometrics, 11: 445–460, (1969).
  • [11] Boubakar, T., Lassina, D., Belco, T., Abdou, F., “The shortest confidence interval for the mean of a normal distribution”, International Journal of Statistics and Probability, 7(2): 33-38, (2018).
  • [12] Khalili, A., Kromp, K., “Statistical properties of Weibull estimators”, Journal of Materials Science, 26(24): 6741-6752, (1991).
  • [13] Tiryakioğlu, M., Hudak, D., “Unbiased estimates of the Weibull parameters by the linear regression method”, Journal of Materials Science, 43: 1914-1919, (2008).
  • [14] Rinne, H. “The Weibull Distribution: A Handbook”, 1st ed, FL: CRC Press, 355-511, (2009).
  • [15] Abernethy, R.B. “The New Weibull Handbook”, 5th ed, North Palm Beach: Fla.R.B. Abernethy, 147-161, (2006).
  • [16] Dodson, B. “The Weibull Analysis Handbook”, 2nd ed., Milwaukee: ASQ Quality Press, 1-167, (2006).
  • [17] McCool, J. I. “Evaluating Weibull endurance data by the method of maximum likelihood”, ASLE Transactions, 13: 189–202, (1970).
  • [18] Murthy, D.N.P, Xie, M., Jiang, R. “Weibull Models”, 1st ed, New York: Wiley, 58-82, (2004).
  • [19] Bergman, B., “On estimation of Weibull modulus”, Journal of Materials Science Letters, 3: 689-692, (1984).
  • [20] Sullivan, J., Lauzon, P., “Experimental probability estimators for Weibull plots”, Journal of Materials Science Letters, 5: 1245–1247, (1986).
  • [21] Wu, D.F., Li, Y.D., Zhang, J.P., Chang, L., Wu, D.H., Fang, Z.P., Shi, Y., “Effects of the number of testing specimens and the estimation methods on the Weibull parameters of solid catalysts”, Chemical Engineering Science, 56: 7035–7044, (2001).
  • [22] Ambrožič, M., Gorjan, L., “Reliability of a Weibull analysis using the maximum-likelihood method”, Journal of Materials Science, 46(6): 1862-1869, (2011).
  • [23] Davies, I.J., “Unbiased estimation of Weibull modulus using linear least squares analysis-A systematic approach”, Journal of the European Ceramic Society, 37(1): 369-380, (2017).
  • [24] Davies, I., “Empirical correction factor for the best estimate of Weibull modulus obtained using linear least squares analysis”, Journal of Materials Science Letters, 20(11): 997-999, (2001).
  • [25] Davies, I., “Best estimate of Weibull modulus obtained using linear least squares analysis: an improved empirical correction factor”, Journal of Materials Science Letters, 39(4): 1441-1444, (2004).
  • [26] Griggs, J., Zhang, Y., “Determining the confidence intervals of Weibull parameters estimated using a more precise probability estimator”, Journal of Materials Science Letters, 22(24): 1771-1773, (2003).
  • [27] Tiryakioğlu, M., “On estimating Weibull modulus by moments and maximum likelihood methods”, Journal of Materials Science, 43(2): 793-798, (2008).
  • [28] Phan, L., McCool, J., “Exact confidence intervals for Weibull parameters and percentiles”, Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability, 223(4): 387-394, (2009).
  • [29] Bütikofer, L., Stawarczykb, B., Roosa, M., “Two regression methods for estimation of a two-parameter Weibull distribution for reliability of dental materials”, Dental Materials, 31: 33-50, (2015).
  • [30] Ferentinos, K.K., Karakostas, K.X., “More on shortest and equal tails confidence intervals”, Communications in Statistics - Theory and Methods, 35: 821–829, (2006).
  • [31] Juola, R. “More on shortest confidence intervals”, The American Statistician, 47(2): 117-119, (1993).
  • [32] Meeker, W. Q., Escobar, L. A. “Statistical methods for reliability data”, New York: Wiley, (1998).
  • [33] Wong, A. C. M., Wu, J. “Practical small-sample asymptotics for distributions used in life-data analysis”, Technometrics, 42: 149–155, (2000).
  • [34] Tse, S.K, Xiang, L., “Interval estimation for Weibull-distributed life data under type II progressive censoring with random removals”, Journal of Biopharmaceutical Statistics, 13(1): 1–16, (2003).
  • [35] Dahiya, R., Guttman, I., “Shortest confidence intervals and prediction intervals for log-normal”, The Canadian Journal of Statistics, 10(4): 277-29, (1982).
  • [36] https://en.wikipedia.org/wiki/Pivotal_quantity. Access Date: 05.04.2022
  • [37] https://docs.pymc.io/notebooks/getting_started.html. Access Date: 05.04.2022
  • [38] https://www.rdocumentation.org/packages/coda/versions/0.19-4/topics/HPDinterval. Access Date: 05.04.2022
  • [39] Birgoren, B., Dirikolu, M.H., “A computer simulation for estimating lower bound fracture strength of composites using Weibull distribution”, Composites Part B: Engineering, 35(3): 263-266, (2004).
  • [40] Papargyris, A.D., “Estimator type and population size for estimating the Weibull modulus in ceramics”, Journal of The European Ceramic Society, 18: 451–455, (1998).
  • [41] Wu, D., Zhou, J., Li, Y., “Unbiased estimation of Weibull parameters with the linear regression method”, Journal of the European Ceramic Society, 26: 1099-1105, (2006).
  • [42] Gong, J., “A new probability index for estimating Weibull modulus for ceramics with the least-square method”, Journal of Materials Science Letters, 19(10): 827-829, (2000).
  • [43] Gong, J., “Determining the confidence intervals for Weibull estimators”, Journal of Materials Science Letters, 18(17): 1405-1407, (1999).
  • [44] Quinn, G.D., “Flexure strength of advanced structural ceramics: a round robin”, Journal of the European Ceramic Society, 73: 2374-2384, (1990).
  • [45] Birgoren, B., “Effect of sample size and distribution parameters in estimation of confidence lower bounds for Weibull percentiles”, Ceramics and Composite Materials: New Research, Ed. B. M. Caruta, Nova Science Publishers, 215-236, (2006).
  • [46] Lu, H.L., Chen, C.H., Wu, J.W., “A note on weighted least-squares estimation of the shape parameter of the Weibull distribution”, Quality and Reliability Engineering International, 20(6): 579–86, (2004).
  • [47] Hung, W.L., “Short communication weighted least-squares estimation of the shape parameter of the Weibull distribution”, Quality and Reliability Engineering International, 17: 467-469, (2001).
  • [48] Bergman, B., “Estimation of Weibull parameters using a weight function”, Journal of Materials Science Letters, 5(6): 611-614, (1986).
  • [49] Faucher, B., Tyson, W., “On the determination of Weibull parameters”, Journal of Materials Science Letters, 7(11): 1199-1203, (1988).
  • [50] Wu, D., Zhou, J., Li, Y., “Methods for estimating Weibull parameters for brittle materials”, Journal of Materials Science, 41: 5630–5638, (2006).
  • [51] Menon, M.V., “Estimation of the shape and scale parameters of the Weibull distribution”, Technometrics, 5: 175-182, (1963).
  • [52] Pratt, J., “Length of confidence intervals”, Journal of the American Statistical Association, 56: 549–567, (1961).
  • [53] Brown, L.D., Casella, G., Hwang, J.T.G., “Optimal confidence sets, bioequivalence, and the Limaçon of Pascal”, Journal of the American Statistical Association, 90: 880-889, (1995).
  • [54] Dirikolu M.H., Aktas¸ A., Birgören B., “Statistical analysis of fracture strength of composite materials using Weibull distribution”, Turkish Journal of Engineering & Environmental Sciences, 26(1): 45–8, (2002).

Shortest Confidence Intervals for Weibull Modulus for Small Samples in Materials Reliability Analysis

Year 2023, Volume: 36 Issue: 1, 284 - 299, 01.03.2023
https://doi.org/10.35378/gujs.916270

Abstract

The Weibull distribution has been widely used to model strength properties of brittle materials. Estimation of confidence intervals for Weibull shape parameter has been an important concern, since small sample sizes in materials science experiments bring about large intervals. Many methods have been proposed in the literature for constructing shorter intervals; the methods of maximum likelihood, least square, and Menon are among the most extensively studied methods. However, they all use an equal-tails approach. The pivotal quantities used for constructing confidence intervals have right-skewed and unimodal distributions, thus, they clearly do not produce the shortest intervals for a given confidence level in equal tail form. This study constructs the shortest confidence intervals for the three aforementioned methods and compares their performances by their equal-tails counterparts. To this end, a comprehensive simulation study has been conducted for the shape parameter values between 1 to 80 and the sample sizes between 3 to 20. The comparison criterion is chosen as the expected interval length. The results show that the shortest confidence intervals in each of three methods have yielded considerably narrower intervals. Further, the unknown parameter values are more centered in these intervals.

References

  • [1] Weibull, W., “A statistical theory of the strength of materials”, Ingvetenskaps Akademiens Handlingar, 151: 1-45, (1939).
  • [2] Barbero, E., Fernández-Sáez, J., Navarro, C., “Statistical analysis of the mechanical properties of composite materials”, Composites Part B: Engineering, 31(5): 375-381, (2000).
  • [3] McCool, J., “Flexural strength tests of brittle materials: Selecting the number of specimens and determining confidence limits for Weibull parameters”, Journal of Testing and Evaluation, 45(2): 664-670, (2016).
  • [4] Barbero, E., Fernández-Sáez, J., Navarro, C., “Statistical distribution of the estimator of Weibull modulus”, Journal of Materials Science Letters, 20(9): 847-849, (2001).
  • [5] McCool, J.I. “Using the Weibull distribution: Reliability, Modeling, and Inference”, 1st ed, NJ: John Wiley & Sons Inc, 97-126, (2012).
  • [6] Casella, G., Berger, R.L. “Statistical Inference”, 2nd ed, USA: Thomson Learning, 417-449, (2002).
  • [7] Guenther, W.C., “Shortest confidence intervals”, The American Statistician, 23: 22-25, (1969).
  • [8] Guenther, W.C., “Unbiased confidence intervals”, The American Statistician, 25: 51-53, (1971).
  • [9] McCool, J.I., “Inference on Weibull percentiles and shape parameter from maximum likelihood estimates”, IEEE Transactions on Reliability, R-19: 2–9, (1970).
  • [10] Thoman, D.R., Bain L.J., Antle C.E., “Inferences on the parameters of the Weibull distribution”, Technometrics, 11: 445–460, (1969).
  • [11] Boubakar, T., Lassina, D., Belco, T., Abdou, F., “The shortest confidence interval for the mean of a normal distribution”, International Journal of Statistics and Probability, 7(2): 33-38, (2018).
  • [12] Khalili, A., Kromp, K., “Statistical properties of Weibull estimators”, Journal of Materials Science, 26(24): 6741-6752, (1991).
  • [13] Tiryakioğlu, M., Hudak, D., “Unbiased estimates of the Weibull parameters by the linear regression method”, Journal of Materials Science, 43: 1914-1919, (2008).
  • [14] Rinne, H. “The Weibull Distribution: A Handbook”, 1st ed, FL: CRC Press, 355-511, (2009).
  • [15] Abernethy, R.B. “The New Weibull Handbook”, 5th ed, North Palm Beach: Fla.R.B. Abernethy, 147-161, (2006).
  • [16] Dodson, B. “The Weibull Analysis Handbook”, 2nd ed., Milwaukee: ASQ Quality Press, 1-167, (2006).
  • [17] McCool, J. I. “Evaluating Weibull endurance data by the method of maximum likelihood”, ASLE Transactions, 13: 189–202, (1970).
  • [18] Murthy, D.N.P, Xie, M., Jiang, R. “Weibull Models”, 1st ed, New York: Wiley, 58-82, (2004).
  • [19] Bergman, B., “On estimation of Weibull modulus”, Journal of Materials Science Letters, 3: 689-692, (1984).
  • [20] Sullivan, J., Lauzon, P., “Experimental probability estimators for Weibull plots”, Journal of Materials Science Letters, 5: 1245–1247, (1986).
  • [21] Wu, D.F., Li, Y.D., Zhang, J.P., Chang, L., Wu, D.H., Fang, Z.P., Shi, Y., “Effects of the number of testing specimens and the estimation methods on the Weibull parameters of solid catalysts”, Chemical Engineering Science, 56: 7035–7044, (2001).
  • [22] Ambrožič, M., Gorjan, L., “Reliability of a Weibull analysis using the maximum-likelihood method”, Journal of Materials Science, 46(6): 1862-1869, (2011).
  • [23] Davies, I.J., “Unbiased estimation of Weibull modulus using linear least squares analysis-A systematic approach”, Journal of the European Ceramic Society, 37(1): 369-380, (2017).
  • [24] Davies, I., “Empirical correction factor for the best estimate of Weibull modulus obtained using linear least squares analysis”, Journal of Materials Science Letters, 20(11): 997-999, (2001).
  • [25] Davies, I., “Best estimate of Weibull modulus obtained using linear least squares analysis: an improved empirical correction factor”, Journal of Materials Science Letters, 39(4): 1441-1444, (2004).
  • [26] Griggs, J., Zhang, Y., “Determining the confidence intervals of Weibull parameters estimated using a more precise probability estimator”, Journal of Materials Science Letters, 22(24): 1771-1773, (2003).
  • [27] Tiryakioğlu, M., “On estimating Weibull modulus by moments and maximum likelihood methods”, Journal of Materials Science, 43(2): 793-798, (2008).
  • [28] Phan, L., McCool, J., “Exact confidence intervals for Weibull parameters and percentiles”, Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability, 223(4): 387-394, (2009).
  • [29] Bütikofer, L., Stawarczykb, B., Roosa, M., “Two regression methods for estimation of a two-parameter Weibull distribution for reliability of dental materials”, Dental Materials, 31: 33-50, (2015).
  • [30] Ferentinos, K.K., Karakostas, K.X., “More on shortest and equal tails confidence intervals”, Communications in Statistics - Theory and Methods, 35: 821–829, (2006).
  • [31] Juola, R. “More on shortest confidence intervals”, The American Statistician, 47(2): 117-119, (1993).
  • [32] Meeker, W. Q., Escobar, L. A. “Statistical methods for reliability data”, New York: Wiley, (1998).
  • [33] Wong, A. C. M., Wu, J. “Practical small-sample asymptotics for distributions used in life-data analysis”, Technometrics, 42: 149–155, (2000).
  • [34] Tse, S.K, Xiang, L., “Interval estimation for Weibull-distributed life data under type II progressive censoring with random removals”, Journal of Biopharmaceutical Statistics, 13(1): 1–16, (2003).
  • [35] Dahiya, R., Guttman, I., “Shortest confidence intervals and prediction intervals for log-normal”, The Canadian Journal of Statistics, 10(4): 277-29, (1982).
  • [36] https://en.wikipedia.org/wiki/Pivotal_quantity. Access Date: 05.04.2022
  • [37] https://docs.pymc.io/notebooks/getting_started.html. Access Date: 05.04.2022
  • [38] https://www.rdocumentation.org/packages/coda/versions/0.19-4/topics/HPDinterval. Access Date: 05.04.2022
  • [39] Birgoren, B., Dirikolu, M.H., “A computer simulation for estimating lower bound fracture strength of composites using Weibull distribution”, Composites Part B: Engineering, 35(3): 263-266, (2004).
  • [40] Papargyris, A.D., “Estimator type and population size for estimating the Weibull modulus in ceramics”, Journal of The European Ceramic Society, 18: 451–455, (1998).
  • [41] Wu, D., Zhou, J., Li, Y., “Unbiased estimation of Weibull parameters with the linear regression method”, Journal of the European Ceramic Society, 26: 1099-1105, (2006).
  • [42] Gong, J., “A new probability index for estimating Weibull modulus for ceramics with the least-square method”, Journal of Materials Science Letters, 19(10): 827-829, (2000).
  • [43] Gong, J., “Determining the confidence intervals for Weibull estimators”, Journal of Materials Science Letters, 18(17): 1405-1407, (1999).
  • [44] Quinn, G.D., “Flexure strength of advanced structural ceramics: a round robin”, Journal of the European Ceramic Society, 73: 2374-2384, (1990).
  • [45] Birgoren, B., “Effect of sample size and distribution parameters in estimation of confidence lower bounds for Weibull percentiles”, Ceramics and Composite Materials: New Research, Ed. B. M. Caruta, Nova Science Publishers, 215-236, (2006).
  • [46] Lu, H.L., Chen, C.H., Wu, J.W., “A note on weighted least-squares estimation of the shape parameter of the Weibull distribution”, Quality and Reliability Engineering International, 20(6): 579–86, (2004).
  • [47] Hung, W.L., “Short communication weighted least-squares estimation of the shape parameter of the Weibull distribution”, Quality and Reliability Engineering International, 17: 467-469, (2001).
  • [48] Bergman, B., “Estimation of Weibull parameters using a weight function”, Journal of Materials Science Letters, 5(6): 611-614, (1986).
  • [49] Faucher, B., Tyson, W., “On the determination of Weibull parameters”, Journal of Materials Science Letters, 7(11): 1199-1203, (1988).
  • [50] Wu, D., Zhou, J., Li, Y., “Methods for estimating Weibull parameters for brittle materials”, Journal of Materials Science, 41: 5630–5638, (2006).
  • [51] Menon, M.V., “Estimation of the shape and scale parameters of the Weibull distribution”, Technometrics, 5: 175-182, (1963).
  • [52] Pratt, J., “Length of confidence intervals”, Journal of the American Statistical Association, 56: 549–567, (1961).
  • [53] Brown, L.D., Casella, G., Hwang, J.T.G., “Optimal confidence sets, bioequivalence, and the Limaçon of Pascal”, Journal of the American Statistical Association, 90: 880-889, (1995).
  • [54] Dirikolu M.H., Aktas¸ A., Birgören B., “Statistical analysis of fracture strength of composite materials using Weibull distribution”, Turkish Journal of Engineering & Environmental Sciences, 26(1): 45–8, (2002).
There are 54 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Industrial Engineering
Authors

Meryem Yalçınkaya 0000-0003-4255-5656

Burak Birgören 0000-0001-9045-6092

Publication Date March 1, 2023
Published in Issue Year 2023 Volume: 36 Issue: 1

Cite

APA Yalçınkaya, M., & Birgören, B. (2023). Shortest Confidence Intervals for Weibull Modulus for Small Samples in Materials Reliability Analysis. Gazi University Journal of Science, 36(1), 284-299. https://doi.org/10.35378/gujs.916270
AMA Yalçınkaya M, Birgören B. Shortest Confidence Intervals for Weibull Modulus for Small Samples in Materials Reliability Analysis. Gazi University Journal of Science. March 2023;36(1):284-299. doi:10.35378/gujs.916270
Chicago Yalçınkaya, Meryem, and Burak Birgören. “Shortest Confidence Intervals for Weibull Modulus for Small Samples in Materials Reliability Analysis”. Gazi University Journal of Science 36, no. 1 (March 2023): 284-99. https://doi.org/10.35378/gujs.916270.
EndNote Yalçınkaya M, Birgören B (March 1, 2023) Shortest Confidence Intervals for Weibull Modulus for Small Samples in Materials Reliability Analysis. Gazi University Journal of Science 36 1 284–299.
IEEE M. Yalçınkaya and B. Birgören, “Shortest Confidence Intervals for Weibull Modulus for Small Samples in Materials Reliability Analysis”, Gazi University Journal of Science, vol. 36, no. 1, pp. 284–299, 2023, doi: 10.35378/gujs.916270.
ISNAD Yalçınkaya, Meryem - Birgören, Burak. “Shortest Confidence Intervals for Weibull Modulus for Small Samples in Materials Reliability Analysis”. Gazi University Journal of Science 36/1 (March 2023), 284-299. https://doi.org/10.35378/gujs.916270.
JAMA Yalçınkaya M, Birgören B. Shortest Confidence Intervals for Weibull Modulus for Small Samples in Materials Reliability Analysis. Gazi University Journal of Science. 2023;36:284–299.
MLA Yalçınkaya, Meryem and Burak Birgören. “Shortest Confidence Intervals for Weibull Modulus for Small Samples in Materials Reliability Analysis”. Gazi University Journal of Science, vol. 36, no. 1, 2023, pp. 284-99, doi:10.35378/gujs.916270.
Vancouver Yalçınkaya M, Birgören B. Shortest Confidence Intervals for Weibull Modulus for Small Samples in Materials Reliability Analysis. Gazi University Journal of Science. 2023;36(1):284-99.