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The Weibull Distribution: A Handbook - Index of

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Contents IX<br />

13 Parameter estimation — More classical approaches and comparisons 476<br />

13.1 Method <strong>of</strong> percentiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476<br />

13.1.1 Two-parameter WEIBULL distribution . . . . . . . . . . . . . . . . 476<br />

13.1.2 Three-parameter WEIBULL distribution . . . . . . . . . . . . . . . 480<br />

13.2 Minimum distance estimators . . . . . . . . . . . . . . . . . . . . . . . . . 485<br />

13.3 Some hybrid estimation methods . . . . . . . . . . . . . . . . . . . . . . . 488<br />

13.4 Miscellaneous approaches . . . . . . . . . . . . . . . . . . . . . . . . . . 491<br />

13.4.1 MENON’s estimators . . . . . . . . . . . . . . . . . . . . . . . . . 491<br />

13.4.2 Block estimators <strong>of</strong> HÜSLER/SCHÜPBACH . . . . . . . . . . . . . 493<br />

13.4.3 KAPPENMAN’s estimators based on the likelihood ratio . . . . . . . 494<br />

13.4.4 KAPPENMAN’s estimators based on sample reuse . . . . . . . . . . 495<br />

13.4.5 Confidence intervals for b and c based on the quantiles <strong>of</strong> beta<br />

distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496<br />

13.4.6 Robust estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 497<br />

13.4.7 Bootstrapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498<br />

13.5 Further estimators for only one <strong>of</strong> the WEIBULL parameters . . . . . . . . 498<br />

13.5.1 Location parameter . . . . . . . . . . . . . . . . . . . . . . . . . . 498<br />

13.5.2 Scale parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501<br />

13.5.3 Shape parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . 503<br />

13.6 Comparisons <strong>of</strong> classical estimators . . . . . . . . . . . . . . . . . . . . . 508<br />

14 Parameter estimation — BAYESIAN approaches 511<br />

14.1 Foundations <strong>of</strong> BAYESIAN inference . . . . . . . . . . . . . . . . . . . . . 511<br />

14.1.1 Types <strong>of</strong> distributions encountered . . . . . . . . . . . . . . . . . . 511<br />

14.1.2 BAYESIAN estimation theory . . . . . . . . . . . . . . . . . . . . . 513<br />

14.1.3 Prior distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 515<br />

14.2 Two-parameter WEIBULL distribution . . . . . . . . . . . . . . . . . . . . 517<br />

14.2.1 Random scale parameter and known shape parameter . . . . . . . . 517<br />

14.2.2 Random shape parameter and known scale parameter . . . . . . . . 525<br />

14.2.3 Random scale and random shape parameters . . . . . . . . . . . . 526<br />

14.3 Empirical BAYES estimation . . . . . . . . . . . . . . . . . . . . . . . . . 528<br />

15 Parameter estimation — Further approaches 531<br />

15.1 Fiducial inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531<br />

© 2009 by Taylor & Francis Group, LLC

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