Development of a kernel density estimation with hybrid estimated bounded data
Abstract Uncertainty quantification, which identifies a probabilistic distribution for uncertain data, is important for yielding accurate and reliable results in reliability analysis and reliability-based design optimization. Sufficient data are needed for accurate uncertainty quantification, but da...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kang, Young-Jin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
Kernel density estimation with estimated bounded data Kernel density estimation with hybrid estimated bounded data |
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| Anmerkung: |
© KSME & Springer 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of mechanical science and technology - Berlin : Springer, 2005, 32(2018), 12 vom: Dez., Seite 5807-5815 |
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| Übergeordnetes Werk: |
volume:32 ; year:2018 ; number:12 ; month:12 ; pages:5807-5815 |
| Links: |
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| DOI / URN: |
10.1007/s12206-018-1128-2 |
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| Katalog-ID: |
SPR025335634 |
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| 100 | 1 | |a Kang, Young-Jin |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Development of a kernel density estimation with hybrid estimated bounded data |
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| 520 | |a Abstract Uncertainty quantification, which identifies a probabilistic distribution for uncertain data, is important for yielding accurate and reliable results in reliability analysis and reliability-based design optimization. Sufficient data are needed for accurate uncertainty quantification, but data is very limited in engineering fields. For statistical modeling using insufficient data, kernel density estimation (KDE) with estimated bounded data (KDE-ebd) has been recently developed for more accurate and conservative estimation than the original KDE by combining given data and bounded data within estimated intervals of random variables from the given data. However, the estimated density function using KDE-ebd is modeled beyond the domain of random variables due to conservative estimation of the density function with long and thick tails. To overcome this problem, this paper proposes kernel density estimation with hybrid estimated bounded data (KDE-Hebd), which does not violate the domain of the random variables, and uses point or interval estimation of the bounds for generating the bounded data. KDE-ebd often yields too wide bounds for very insufficient data or large variations because it uses only the estimated intervals of random variables. The proposed KDE with hybrid estimated bounded data alternatively selects a point estimator or interval estimator according to whether the estimated intervals violate the domain of the random variables. The performance of the proposed method was evaluated by comparing the estimation accuracy from statistical simulation tests for mathematically derived sample data and real experimental data using KDE, KDE-ebd and KDE-Hebd. As a result, it was demonstrated that KDE-Hebd was more accurate than KDE-ebd without the violation of the domain of random variables, especially for a large coefficient of variation. | ||
| 650 | 4 | |a Statistical modeling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Kernel density estimation with estimated bounded data |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Kernel density estimation with hybrid estimated bounded data |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Point estimation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Interval estimation |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Noh, Yoojeong |4 aut | |
| 700 | 1 | |a Lim, O-Kaung |4 aut | |
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10.1007/s12206-018-1128-2 doi (DE-627)SPR025335634 (SPR)s12206-018-1128-2-e DE-627 ger DE-627 rakwb eng Kang, Young-Jin verfasserin aut Development of a kernel density estimation with hybrid estimated bounded data 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © KSME & Springer 2018 Abstract Uncertainty quantification, which identifies a probabilistic distribution for uncertain data, is important for yielding accurate and reliable results in reliability analysis and reliability-based design optimization. Sufficient data are needed for accurate uncertainty quantification, but data is very limited in engineering fields. For statistical modeling using insufficient data, kernel density estimation (KDE) with estimated bounded data (KDE-ebd) has been recently developed for more accurate and conservative estimation than the original KDE by combining given data and bounded data within estimated intervals of random variables from the given data. However, the estimated density function using KDE-ebd is modeled beyond the domain of random variables due to conservative estimation of the density function with long and thick tails. To overcome this problem, this paper proposes kernel density estimation with hybrid estimated bounded data (KDE-Hebd), which does not violate the domain of the random variables, and uses point or interval estimation of the bounds for generating the bounded data. KDE-ebd often yields too wide bounds for very insufficient data or large variations because it uses only the estimated intervals of random variables. The proposed KDE with hybrid estimated bounded data alternatively selects a point estimator or interval estimator according to whether the estimated intervals violate the domain of the random variables. The performance of the proposed method was evaluated by comparing the estimation accuracy from statistical simulation tests for mathematically derived sample data and real experimental data using KDE, KDE-ebd and KDE-Hebd. As a result, it was demonstrated that KDE-Hebd was more accurate than KDE-ebd without the violation of the domain of random variables, especially for a large coefficient of variation. Statistical modeling (dpeaa)DE-He213 Kernel density estimation with estimated bounded data (dpeaa)DE-He213 Kernel density estimation with hybrid estimated bounded data (dpeaa)DE-He213 Point estimation (dpeaa)DE-He213 Interval estimation (dpeaa)DE-He213 Noh, Yoojeong aut Lim, O-Kaung aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 32(2018), 12 vom: Dez., Seite 5807-5815 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:32 year:2018 number:12 month:12 pages:5807-5815 https://dx.doi.org/10.1007/s12206-018-1128-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2018 12 12 5807-5815 |
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10.1007/s12206-018-1128-2 doi (DE-627)SPR025335634 (SPR)s12206-018-1128-2-e DE-627 ger DE-627 rakwb eng Kang, Young-Jin verfasserin aut Development of a kernel density estimation with hybrid estimated bounded data 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © KSME & Springer 2018 Abstract Uncertainty quantification, which identifies a probabilistic distribution for uncertain data, is important for yielding accurate and reliable results in reliability analysis and reliability-based design optimization. Sufficient data are needed for accurate uncertainty quantification, but data is very limited in engineering fields. For statistical modeling using insufficient data, kernel density estimation (KDE) with estimated bounded data (KDE-ebd) has been recently developed for more accurate and conservative estimation than the original KDE by combining given data and bounded data within estimated intervals of random variables from the given data. However, the estimated density function using KDE-ebd is modeled beyond the domain of random variables due to conservative estimation of the density function with long and thick tails. To overcome this problem, this paper proposes kernel density estimation with hybrid estimated bounded data (KDE-Hebd), which does not violate the domain of the random variables, and uses point or interval estimation of the bounds for generating the bounded data. KDE-ebd often yields too wide bounds for very insufficient data or large variations because it uses only the estimated intervals of random variables. The proposed KDE with hybrid estimated bounded data alternatively selects a point estimator or interval estimator according to whether the estimated intervals violate the domain of the random variables. The performance of the proposed method was evaluated by comparing the estimation accuracy from statistical simulation tests for mathematically derived sample data and real experimental data using KDE, KDE-ebd and KDE-Hebd. As a result, it was demonstrated that KDE-Hebd was more accurate than KDE-ebd without the violation of the domain of random variables, especially for a large coefficient of variation. Statistical modeling (dpeaa)DE-He213 Kernel density estimation with estimated bounded data (dpeaa)DE-He213 Kernel density estimation with hybrid estimated bounded data (dpeaa)DE-He213 Point estimation (dpeaa)DE-He213 Interval estimation (dpeaa)DE-He213 Noh, Yoojeong aut Lim, O-Kaung aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 32(2018), 12 vom: Dez., Seite 5807-5815 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:32 year:2018 number:12 month:12 pages:5807-5815 https://dx.doi.org/10.1007/s12206-018-1128-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2018 12 12 5807-5815 |
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10.1007/s12206-018-1128-2 doi (DE-627)SPR025335634 (SPR)s12206-018-1128-2-e DE-627 ger DE-627 rakwb eng Kang, Young-Jin verfasserin aut Development of a kernel density estimation with hybrid estimated bounded data 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © KSME & Springer 2018 Abstract Uncertainty quantification, which identifies a probabilistic distribution for uncertain data, is important for yielding accurate and reliable results in reliability analysis and reliability-based design optimization. Sufficient data are needed for accurate uncertainty quantification, but data is very limited in engineering fields. For statistical modeling using insufficient data, kernel density estimation (KDE) with estimated bounded data (KDE-ebd) has been recently developed for more accurate and conservative estimation than the original KDE by combining given data and bounded data within estimated intervals of random variables from the given data. However, the estimated density function using KDE-ebd is modeled beyond the domain of random variables due to conservative estimation of the density function with long and thick tails. To overcome this problem, this paper proposes kernel density estimation with hybrid estimated bounded data (KDE-Hebd), which does not violate the domain of the random variables, and uses point or interval estimation of the bounds for generating the bounded data. KDE-ebd often yields too wide bounds for very insufficient data or large variations because it uses only the estimated intervals of random variables. The proposed KDE with hybrid estimated bounded data alternatively selects a point estimator or interval estimator according to whether the estimated intervals violate the domain of the random variables. The performance of the proposed method was evaluated by comparing the estimation accuracy from statistical simulation tests for mathematically derived sample data and real experimental data using KDE, KDE-ebd and KDE-Hebd. As a result, it was demonstrated that KDE-Hebd was more accurate than KDE-ebd without the violation of the domain of random variables, especially for a large coefficient of variation. Statistical modeling (dpeaa)DE-He213 Kernel density estimation with estimated bounded data (dpeaa)DE-He213 Kernel density estimation with hybrid estimated bounded data (dpeaa)DE-He213 Point estimation (dpeaa)DE-He213 Interval estimation (dpeaa)DE-He213 Noh, Yoojeong aut Lim, O-Kaung aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 32(2018), 12 vom: Dez., Seite 5807-5815 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:32 year:2018 number:12 month:12 pages:5807-5815 https://dx.doi.org/10.1007/s12206-018-1128-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2018 12 12 5807-5815 |
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10.1007/s12206-018-1128-2 doi (DE-627)SPR025335634 (SPR)s12206-018-1128-2-e DE-627 ger DE-627 rakwb eng Kang, Young-Jin verfasserin aut Development of a kernel density estimation with hybrid estimated bounded data 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © KSME & Springer 2018 Abstract Uncertainty quantification, which identifies a probabilistic distribution for uncertain data, is important for yielding accurate and reliable results in reliability analysis and reliability-based design optimization. Sufficient data are needed for accurate uncertainty quantification, but data is very limited in engineering fields. For statistical modeling using insufficient data, kernel density estimation (KDE) with estimated bounded data (KDE-ebd) has been recently developed for more accurate and conservative estimation than the original KDE by combining given data and bounded data within estimated intervals of random variables from the given data. However, the estimated density function using KDE-ebd is modeled beyond the domain of random variables due to conservative estimation of the density function with long and thick tails. To overcome this problem, this paper proposes kernel density estimation with hybrid estimated bounded data (KDE-Hebd), which does not violate the domain of the random variables, and uses point or interval estimation of the bounds for generating the bounded data. KDE-ebd often yields too wide bounds for very insufficient data or large variations because it uses only the estimated intervals of random variables. The proposed KDE with hybrid estimated bounded data alternatively selects a point estimator or interval estimator according to whether the estimated intervals violate the domain of the random variables. The performance of the proposed method was evaluated by comparing the estimation accuracy from statistical simulation tests for mathematically derived sample data and real experimental data using KDE, KDE-ebd and KDE-Hebd. As a result, it was demonstrated that KDE-Hebd was more accurate than KDE-ebd without the violation of the domain of random variables, especially for a large coefficient of variation. Statistical modeling (dpeaa)DE-He213 Kernel density estimation with estimated bounded data (dpeaa)DE-He213 Kernel density estimation with hybrid estimated bounded data (dpeaa)DE-He213 Point estimation (dpeaa)DE-He213 Interval estimation (dpeaa)DE-He213 Noh, Yoojeong aut Lim, O-Kaung aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 32(2018), 12 vom: Dez., Seite 5807-5815 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:32 year:2018 number:12 month:12 pages:5807-5815 https://dx.doi.org/10.1007/s12206-018-1128-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2018 12 12 5807-5815 |
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10.1007/s12206-018-1128-2 doi (DE-627)SPR025335634 (SPR)s12206-018-1128-2-e DE-627 ger DE-627 rakwb eng Kang, Young-Jin verfasserin aut Development of a kernel density estimation with hybrid estimated bounded data 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © KSME & Springer 2018 Abstract Uncertainty quantification, which identifies a probabilistic distribution for uncertain data, is important for yielding accurate and reliable results in reliability analysis and reliability-based design optimization. Sufficient data are needed for accurate uncertainty quantification, but data is very limited in engineering fields. For statistical modeling using insufficient data, kernel density estimation (KDE) with estimated bounded data (KDE-ebd) has been recently developed for more accurate and conservative estimation than the original KDE by combining given data and bounded data within estimated intervals of random variables from the given data. However, the estimated density function using KDE-ebd is modeled beyond the domain of random variables due to conservative estimation of the density function with long and thick tails. To overcome this problem, this paper proposes kernel density estimation with hybrid estimated bounded data (KDE-Hebd), which does not violate the domain of the random variables, and uses point or interval estimation of the bounds for generating the bounded data. KDE-ebd often yields too wide bounds for very insufficient data or large variations because it uses only the estimated intervals of random variables. The proposed KDE with hybrid estimated bounded data alternatively selects a point estimator or interval estimator according to whether the estimated intervals violate the domain of the random variables. The performance of the proposed method was evaluated by comparing the estimation accuracy from statistical simulation tests for mathematically derived sample data and real experimental data using KDE, KDE-ebd and KDE-Hebd. As a result, it was demonstrated that KDE-Hebd was more accurate than KDE-ebd without the violation of the domain of random variables, especially for a large coefficient of variation. Statistical modeling (dpeaa)DE-He213 Kernel density estimation with estimated bounded data (dpeaa)DE-He213 Kernel density estimation with hybrid estimated bounded data (dpeaa)DE-He213 Point estimation (dpeaa)DE-He213 Interval estimation (dpeaa)DE-He213 Noh, Yoojeong aut Lim, O-Kaung aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 32(2018), 12 vom: Dez., Seite 5807-5815 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:32 year:2018 number:12 month:12 pages:5807-5815 https://dx.doi.org/10.1007/s12206-018-1128-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2018 12 12 5807-5815 |
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Kang, Young-Jin @@aut@@ Noh, Yoojeong @@aut@@ Lim, O-Kaung @@aut@@ |
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Sufficient data are needed for accurate uncertainty quantification, but data is very limited in engineering fields. For statistical modeling using insufficient data, kernel density estimation (KDE) with estimated bounded data (KDE-ebd) has been recently developed for more accurate and conservative estimation than the original KDE by combining given data and bounded data within estimated intervals of random variables from the given data. However, the estimated density function using KDE-ebd is modeled beyond the domain of random variables due to conservative estimation of the density function with long and thick tails. To overcome this problem, this paper proposes kernel density estimation with hybrid estimated bounded data (KDE-Hebd), which does not violate the domain of the random variables, and uses point or interval estimation of the bounds for generating the bounded data. KDE-ebd often yields too wide bounds for very insufficient data or large variations because it uses only the estimated intervals of random variables. The proposed KDE with hybrid estimated bounded data alternatively selects a point estimator or interval estimator according to whether the estimated intervals violate the domain of the random variables. The performance of the proposed method was evaluated by comparing the estimation accuracy from statistical simulation tests for mathematically derived sample data and real experimental data using KDE, KDE-ebd and KDE-Hebd. 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Kang, Young-Jin |
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Kang, Young-Jin misc Statistical modeling misc Kernel density estimation with estimated bounded data misc Kernel density estimation with hybrid estimated bounded data misc Point estimation misc Interval estimation Development of a kernel density estimation with hybrid estimated bounded data |
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Development of a kernel density estimation with hybrid estimated bounded data Statistical modeling (dpeaa)DE-He213 Kernel density estimation with estimated bounded data (dpeaa)DE-He213 Kernel density estimation with hybrid estimated bounded data (dpeaa)DE-He213 Point estimation (dpeaa)DE-He213 Interval estimation (dpeaa)DE-He213 |
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misc Statistical modeling misc Kernel density estimation with estimated bounded data misc Kernel density estimation with hybrid estimated bounded data misc Point estimation misc Interval estimation |
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development of a kernel density estimation with hybrid estimated bounded data |
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Development of a kernel density estimation with hybrid estimated bounded data |
| abstract |
Abstract Uncertainty quantification, which identifies a probabilistic distribution for uncertain data, is important for yielding accurate and reliable results in reliability analysis and reliability-based design optimization. Sufficient data are needed for accurate uncertainty quantification, but data is very limited in engineering fields. For statistical modeling using insufficient data, kernel density estimation (KDE) with estimated bounded data (KDE-ebd) has been recently developed for more accurate and conservative estimation than the original KDE by combining given data and bounded data within estimated intervals of random variables from the given data. However, the estimated density function using KDE-ebd is modeled beyond the domain of random variables due to conservative estimation of the density function with long and thick tails. To overcome this problem, this paper proposes kernel density estimation with hybrid estimated bounded data (KDE-Hebd), which does not violate the domain of the random variables, and uses point or interval estimation of the bounds for generating the bounded data. KDE-ebd often yields too wide bounds for very insufficient data or large variations because it uses only the estimated intervals of random variables. The proposed KDE with hybrid estimated bounded data alternatively selects a point estimator or interval estimator according to whether the estimated intervals violate the domain of the random variables. The performance of the proposed method was evaluated by comparing the estimation accuracy from statistical simulation tests for mathematically derived sample data and real experimental data using KDE, KDE-ebd and KDE-Hebd. As a result, it was demonstrated that KDE-Hebd was more accurate than KDE-ebd without the violation of the domain of random variables, especially for a large coefficient of variation. © KSME & Springer 2018 |
| abstractGer |
Abstract Uncertainty quantification, which identifies a probabilistic distribution for uncertain data, is important for yielding accurate and reliable results in reliability analysis and reliability-based design optimization. Sufficient data are needed for accurate uncertainty quantification, but data is very limited in engineering fields. For statistical modeling using insufficient data, kernel density estimation (KDE) with estimated bounded data (KDE-ebd) has been recently developed for more accurate and conservative estimation than the original KDE by combining given data and bounded data within estimated intervals of random variables from the given data. However, the estimated density function using KDE-ebd is modeled beyond the domain of random variables due to conservative estimation of the density function with long and thick tails. To overcome this problem, this paper proposes kernel density estimation with hybrid estimated bounded data (KDE-Hebd), which does not violate the domain of the random variables, and uses point or interval estimation of the bounds for generating the bounded data. KDE-ebd often yields too wide bounds for very insufficient data or large variations because it uses only the estimated intervals of random variables. The proposed KDE with hybrid estimated bounded data alternatively selects a point estimator or interval estimator according to whether the estimated intervals violate the domain of the random variables. The performance of the proposed method was evaluated by comparing the estimation accuracy from statistical simulation tests for mathematically derived sample data and real experimental data using KDE, KDE-ebd and KDE-Hebd. As a result, it was demonstrated that KDE-Hebd was more accurate than KDE-ebd without the violation of the domain of random variables, especially for a large coefficient of variation. © KSME & Springer 2018 |
| abstract_unstemmed |
Abstract Uncertainty quantification, which identifies a probabilistic distribution for uncertain data, is important for yielding accurate and reliable results in reliability analysis and reliability-based design optimization. Sufficient data are needed for accurate uncertainty quantification, but data is very limited in engineering fields. For statistical modeling using insufficient data, kernel density estimation (KDE) with estimated bounded data (KDE-ebd) has been recently developed for more accurate and conservative estimation than the original KDE by combining given data and bounded data within estimated intervals of random variables from the given data. However, the estimated density function using KDE-ebd is modeled beyond the domain of random variables due to conservative estimation of the density function with long and thick tails. To overcome this problem, this paper proposes kernel density estimation with hybrid estimated bounded data (KDE-Hebd), which does not violate the domain of the random variables, and uses point or interval estimation of the bounds for generating the bounded data. KDE-ebd often yields too wide bounds for very insufficient data or large variations because it uses only the estimated intervals of random variables. The proposed KDE with hybrid estimated bounded data alternatively selects a point estimator or interval estimator according to whether the estimated intervals violate the domain of the random variables. The performance of the proposed method was evaluated by comparing the estimation accuracy from statistical simulation tests for mathematically derived sample data and real experimental data using KDE, KDE-ebd and KDE-Hebd. As a result, it was demonstrated that KDE-Hebd was more accurate than KDE-ebd without the violation of the domain of random variables, especially for a large coefficient of variation. © KSME & Springer 2018 |
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Development of a kernel density estimation with hybrid estimated bounded data |
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https://dx.doi.org/10.1007/s12206-018-1128-2 |
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Noh, Yoojeong Lim, O-Kaung |
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10.1007/s12206-018-1128-2 |
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2024-07-03T15:21:45.088Z |
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| score |
7.3996353 |