Statistical inference for type I multiply left-censored samples from Weibull distribution*

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG42__%2F19%3A00536928" target="_blank" >RIV/60162694:G42__/19:00536928 - isvavai.cz</a>
Alternative codes found
RIV/00216305:26220/19:PU132623
Result on the web
<a href="http://www.kibernetika.org/annotations/2019/19referats4.pdf" target="_blank" >http://www.kibernetika.org/annotations/2019/19referats4.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10559-019-00168-x" target="_blank" >10.1007/s10559-019-00168-x</a>

Result language
angličtina
Original language name
Statistical inference for type I multiply left-censored samples from Weibull distribution*
Original language description
Left-censored data with one or more detection limits occur frequently in many application areas. In this paper, the computational procedure for calculation of maximum likelihood estimates of the parameters for type I multiply left-censored data from underlying Weibull distribution is suggested and used considering various numbers of detection limits. The expected Fisher information matrix is analytically determined and its performance is compared with sample (observed) Fisher information matrix using simulations. Simulations are focused primarily on the properties of estimators for small sample sizes. Real data illustration is included.
Czech name
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Czech description
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Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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