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FUSEK, M. MICHÁLEK, J.
Originální název
Asymptotic Tests for Multiply Left-Censored Samples from Weibull Distribution
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
Left-censored data with one or more detection limits occur frequently in many application areas. This paper suggests the computational procedure for calculation of maximum likelihood estimates of the parameters and estimates of their variances for type I multiply left-censored Weibull samples. Estimates of the variances of estimated parameters are based on the analytically determined expected Fisher information matrix. Moreover, using the asymptotic properties of maximum likelihood estimates and tests with nuisance parameters (Lagrange multiplier test, likelihood ratio test, Wald test), methods for comparison of two independent type I multiply left-censored Weibull samples are proposed. The power functions of particular tests are compared by simulations. The methods derived in this paper can be used in real environmental or chemical data analysis.
Klíčová slova
Fisher information matrix, maximum likelihood, musk compounds, power of test
Autoři
FUSEK, M.; MICHÁLEK, J.
Rok RIV
2014
Vydáno
25. 6. 2014
Nakladatel
Brno University of Technology, Faculty of Mechanical Engineering, Institute of Automation and Computer Science
Místo
Brno, Czech Republic
ISBN
978-80-214-4984-8
Kniha
MENDEL 2014, 20th International Conference on Soft Computing
ISSN
1803-3814
Periodikum
Mendel Journal series
Stát
Česká republika
Strany od
317
Strany do
322
Strany počet
6
BibTex
@inproceedings{BUT108106, author="Michal {Fusek} and Jaroslav {Michálek}", title="Asymptotic Tests for Multiply Left-Censored Samples from Weibull Distribution", booktitle="MENDEL 2014, 20th International Conference on Soft Computing", year="2014", journal="Mendel Journal series", pages="317--322", publisher="Brno University of Technology, Faculty of Mechanical Engineering, Institute of Automation and Computer Science", address="Brno, Czech Republic", isbn="978-80-214-4984-8", issn="1803-3814" }