Smooth Estimators of the Reliability Functions for Non-Restorable Elements
Empirical distribution and reliability functions are discrete that often does not correspond to real random variables in physical applications. Smooth nonparametric estimators of the reliability function based on finite and Laplace kernel functions are suggested. The asymptotic mean square error of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Koshkin, G. M. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2014 |
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| Übergeordnetes Werk: |
Enthalten in: Russian physics journal - Springer US, 1992, 57(2014), 5 vom: Sept., Seite 672-681 |
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| Übergeordnetes Werk: |
volume:57 ; year:2014 ; number:5 ; month:09 ; pages:672-681 |
| Links: |
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| DOI / URN: |
10.1007/s11182-014-0290-y |
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OLC2033080633 |
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| 520 | |a Empirical distribution and reliability functions are discrete that often does not correspond to real random variables in physical applications. Smooth nonparametric estimators of the reliability function based on finite and Laplace kernel functions are suggested. The asymptotic mean square error of the estimator and its limiting distribution are presented that allow a new interval estimation of the reliability function to be constructed. Advantages of the suggested estimators over the well-known parametric algorithms for calculations of the strength reliability are discussed. | ||
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| 650 | 4 | |a mean square error (MSE) | |
| 650 | 4 | |a asymptotic normality | |
| 650 | 4 | |a interval estimation | |
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10.1007/s11182-014-0290-y doi (DE-627)OLC2033080633 (DE-He213)s11182-014-0290-y-p DE-627 ger DE-627 rakwb eng 530 370 VZ Koshkin, G. M. verfasserin aut Smooth Estimators of the Reliability Functions for Non-Restorable Elements 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2014 Empirical distribution and reliability functions are discrete that often does not correspond to real random variables in physical applications. Smooth nonparametric estimators of the reliability function based on finite and Laplace kernel functions are suggested. The asymptotic mean square error of the estimator and its limiting distribution are presented that allow a new interval estimation of the reliability function to be constructed. Advantages of the suggested estimators over the well-known parametric algorithms for calculations of the strength reliability are discussed. reliability function smooth kernel estimator mean square error (MSE) asymptotic normality interval estimation Enthalten in Russian physics journal Springer US, 1992 57(2014), 5 vom: Sept., Seite 672-681 (DE-627)131169718 (DE-600)1138228-4 (DE-576)033029253 1064-8887 nnns volume:57 year:2014 number:5 month:09 pages:672-681 https://doi.org/10.1007/s11182-014-0290-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY GBV_ILN_70 AR 57 2014 5 09 672-681 |
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10.1007/s11182-014-0290-y doi (DE-627)OLC2033080633 (DE-He213)s11182-014-0290-y-p DE-627 ger DE-627 rakwb eng 530 370 VZ Koshkin, G. M. verfasserin aut Smooth Estimators of the Reliability Functions for Non-Restorable Elements 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2014 Empirical distribution and reliability functions are discrete that often does not correspond to real random variables in physical applications. Smooth nonparametric estimators of the reliability function based on finite and Laplace kernel functions are suggested. The asymptotic mean square error of the estimator and its limiting distribution are presented that allow a new interval estimation of the reliability function to be constructed. Advantages of the suggested estimators over the well-known parametric algorithms for calculations of the strength reliability are discussed. reliability function smooth kernel estimator mean square error (MSE) asymptotic normality interval estimation Enthalten in Russian physics journal Springer US, 1992 57(2014), 5 vom: Sept., Seite 672-681 (DE-627)131169718 (DE-600)1138228-4 (DE-576)033029253 1064-8887 nnns volume:57 year:2014 number:5 month:09 pages:672-681 https://doi.org/10.1007/s11182-014-0290-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY GBV_ILN_70 AR 57 2014 5 09 672-681 |
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10.1007/s11182-014-0290-y doi (DE-627)OLC2033080633 (DE-He213)s11182-014-0290-y-p DE-627 ger DE-627 rakwb eng 530 370 VZ Koshkin, G. M. verfasserin aut Smooth Estimators of the Reliability Functions for Non-Restorable Elements 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2014 Empirical distribution and reliability functions are discrete that often does not correspond to real random variables in physical applications. Smooth nonparametric estimators of the reliability function based on finite and Laplace kernel functions are suggested. The asymptotic mean square error of the estimator and its limiting distribution are presented that allow a new interval estimation of the reliability function to be constructed. Advantages of the suggested estimators over the well-known parametric algorithms for calculations of the strength reliability are discussed. reliability function smooth kernel estimator mean square error (MSE) asymptotic normality interval estimation Enthalten in Russian physics journal Springer US, 1992 57(2014), 5 vom: Sept., Seite 672-681 (DE-627)131169718 (DE-600)1138228-4 (DE-576)033029253 1064-8887 nnns volume:57 year:2014 number:5 month:09 pages:672-681 https://doi.org/10.1007/s11182-014-0290-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY GBV_ILN_70 AR 57 2014 5 09 672-681 |
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Empirical distribution and reliability functions are discrete that often does not correspond to real random variables in physical applications. Smooth nonparametric estimators of the reliability function based on finite and Laplace kernel functions are suggested. The asymptotic mean square error of the estimator and its limiting distribution are presented that allow a new interval estimation of the reliability function to be constructed. Advantages of the suggested estimators over the well-known parametric algorithms for calculations of the strength reliability are discussed. © Springer Science+Business Media New York 2014 |
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Empirical distribution and reliability functions are discrete that often does not correspond to real random variables in physical applications. Smooth nonparametric estimators of the reliability function based on finite and Laplace kernel functions are suggested. The asymptotic mean square error of the estimator and its limiting distribution are presented that allow a new interval estimation of the reliability function to be constructed. Advantages of the suggested estimators over the well-known parametric algorithms for calculations of the strength reliability are discussed. © Springer Science+Business Media New York 2014 |
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Empirical distribution and reliability functions are discrete that often does not correspond to real random variables in physical applications. Smooth nonparametric estimators of the reliability function based on finite and Laplace kernel functions are suggested. The asymptotic mean square error of the estimator and its limiting distribution are presented that allow a new interval estimation of the reliability function to be constructed. Advantages of the suggested estimators over the well-known parametric algorithms for calculations of the strength reliability are discussed. © Springer Science+Business Media New York 2014 |
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M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Smooth Estimators of the Reliability Functions for Non-Restorable Elements</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media New York 2014</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Empirical distribution and reliability functions are discrete that often does not correspond to real random variables in physical applications. Smooth nonparametric estimators of the reliability function based on finite and Laplace kernel functions are suggested. The asymptotic mean square error of the estimator and its limiting distribution are presented that allow a new interval estimation of the reliability function to be constructed. Advantages of the suggested estimators over the well-known parametric algorithms for calculations of the strength reliability are discussed.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">reliability function</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">smooth kernel estimator</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">mean square error (MSE)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">asymptotic normality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">interval estimation</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Russian physics journal</subfield><subfield code="d">Springer US, 1992</subfield><subfield code="g">57(2014), 5 vom: Sept., Seite 672-681</subfield><subfield code="w">(DE-627)131169718</subfield><subfield code="w">(DE-600)1138228-4</subfield><subfield code="w">(DE-576)033029253</subfield><subfield code="x">1064-8887</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:57</subfield><subfield code="g">year:2014</subfield><subfield code="g">number:5</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:672-681</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11182-014-0290-y</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">57</subfield><subfield code="j">2014</subfield><subfield code="e">5</subfield><subfield code="c">09</subfield><subfield code="h">672-681</subfield></datafield></record></collection>
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