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Two regression methods for estimation of a two-parameter Weibull distribution for reliability of dental materials


Bütikofer, Lukas; Stawarczyk, Bogna; Roos, Malgorzata (2015). Two regression methods for estimation of a two-parameter Weibull distribution for reliability of dental materials. Dental Materials, 31(2):e33-e50.

Abstract

OBJECTIVES Comparison of estimation of the two-parameter Weibull distribution by two least squares (LS) methods with interchanged axes. Investigation of the influence of plotting positions and sample size. Derivation of 95% confidence intervals (95%CI) for Weibull parameters applicable in the context of LS estimation. Preparation of a free available Excel template for computation of point estimates and 95%CI for Weibull modulus (m) and characteristic strength (s). METHODS Monte Carlo simulation covering a wide range of Weibull parameters and sample sizes. Mathematical derivation of formulae for computation of 95%CI according to a Menon-type approach for both m and s. Empirical proof that the practically observed coverage agrees with the nominal one of 95%. RESULTS Relative and absolute performance of LS estimators depended on sample size, plotting positions and parameter to be estimated. For most situations they outperformed the corresponding Maximum Likelihood (ML) estimator in terms of bias, while precision was almost the same. Naïve Wald-type 95%CI based on standard errors of LS regression coefficients did not reach targeted coverage. An easy-to-apply alternative based on asymptotic standard errors (Menon 95%CI) resulted in excellent coverage. CONCLUSION Accuracy of the LS methods for Weibull modulus and characteristic strength essentially depend on plotting position and sample size. Large sample sizes (n≥30) support a credible Weibull parameters estimation. An important complement of the point estimates of Weibull parameters is provided by the Menon 95%CI. A free available Excel template considerably facilitating computation of point and interval estimates of Weibull parameters is provided.

Abstract

OBJECTIVES Comparison of estimation of the two-parameter Weibull distribution by two least squares (LS) methods with interchanged axes. Investigation of the influence of plotting positions and sample size. Derivation of 95% confidence intervals (95%CI) for Weibull parameters applicable in the context of LS estimation. Preparation of a free available Excel template for computation of point estimates and 95%CI for Weibull modulus (m) and characteristic strength (s). METHODS Monte Carlo simulation covering a wide range of Weibull parameters and sample sizes. Mathematical derivation of formulae for computation of 95%CI according to a Menon-type approach for both m and s. Empirical proof that the practically observed coverage agrees with the nominal one of 95%. RESULTS Relative and absolute performance of LS estimators depended on sample size, plotting positions and parameter to be estimated. For most situations they outperformed the corresponding Maximum Likelihood (ML) estimator in terms of bias, while precision was almost the same. Naïve Wald-type 95%CI based on standard errors of LS regression coefficients did not reach targeted coverage. An easy-to-apply alternative based on asymptotic standard errors (Menon 95%CI) resulted in excellent coverage. CONCLUSION Accuracy of the LS methods for Weibull modulus and characteristic strength essentially depend on plotting position and sample size. Large sample sizes (n≥30) support a credible Weibull parameters estimation. An important complement of the point estimates of Weibull parameters is provided by the Menon 95%CI. A free available Excel template considerably facilitating computation of point and interval estimates of Weibull parameters is provided.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:February 2015
Deposited On:05 Mar 2015 16:18
Last Modified:05 Apr 2016 19:09
Publisher:Elsevier
ISSN:0109-5641
Publisher DOI:https://doi.org/10.1016/j.dental.2014.11.014
PubMed ID:25499248

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