Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data
Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental varia...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hong, Yili [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2015 |
|---|
| Rechteinformationen: |
Nutzungsrecht: Copyright © American Statistical Association and the American Society for Quality 2015 |
|---|
| Schlagwörter: |
|---|
| Systematik: |
|
|---|
| Übergeordnetes Werk: |
Enthalten in: Technometrics - Philadelphia, Pa. : Taylor and Francis Group, 1959, 57(2015), 2, Seite 180 |
|---|---|
| Übergeordnetes Werk: |
volume:57 ; year:2015 ; number:2 ; pages:180 |
| Links: |
|---|
| DOI / URN: |
10.1080/00401706.2014.915891 |
|---|
| Katalog-ID: |
OLC1966302592 |
|---|
| LEADER | 01000caa a2200265 4500 | ||
|---|---|---|---|
| 001 | OLC1966302592 | ||
| 003 | DE-627 | ||
| 005 | 20230714165219.0 | ||
| 007 | tu | ||
| 008 | 160206s2015 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1080/00401706.2014.915891 |2 doi | |
| 028 | 5 | 2 | |a PQ20160617 |
| 035 | |a (DE-627)OLC1966302592 | ||
| 035 | |a (DE-599)GBVOLC1966302592 | ||
| 035 | |a (PRQ)c1756-cbfc8043e824eeb91bb6ace630ccdf8a9624104483e698be94a2adc50931cec40 | ||
| 035 | |a (KEY)0056224620150000057000200180statisticalmethodsfordegradationdatawithdynamiccov | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 600 |q DNB |
| 084 | |a SA 8300 |q AVZ |2 rvk | ||
| 100 | 1 | |a Hong, Yili |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data |
| 264 | 1 | |c 2015 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 520 | |a Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online. | ||
| 540 | |a Nutzungsrecht: Copyright © American Statistical Association and the American Society for Quality 2015 | ||
| 650 | 4 | |a Organic coatings | |
| 650 | 4 | |a Lifetime prediction | |
| 650 | 4 | |a Usage history | |
| 650 | 4 | |a Environmental conditions | |
| 650 | 4 | |a Covariate process | |
| 650 | 4 | |a System health monitoring | |
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Maximum likelihood method | |
| 650 | 4 | |a Measurement | |
| 650 | 4 | |a Reliability | |
| 650 | 4 | |a Time series | |
| 700 | 1 | |a Duan, Yuanyuan |4 oth | |
| 700 | 1 | |a Meeker, William Q |4 oth | |
| 700 | 1 | |a Stanley, Deborah L |4 oth | |
| 700 | 1 | |a Gu, Xiaohong |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t Technometrics |d Philadelphia, Pa. : Taylor and Francis Group, 1959 |g 57(2015), 2, Seite 180 |w (DE-627)129480088 |w (DE-600)204242-3 |w (DE-576)014862395 |x 0040-1706 |7 nnns |
| 773 | 1 | 8 | |g volume:57 |g year:2015 |g number:2 |g pages:180 |
| 856 | 4 | 1 | |u http://dx.doi.org/10.1080/00401706.2014.915891 |3 Volltext |
| 856 | 4 | 2 | |u http://www.tandfonline.com/doi/abs/10.1080/00401706.2014.915891 |
| 856 | 4 | 2 | |u http://search.proquest.com/docview/1697516931 |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a SSG-OLC-PHY | ||
| 912 | |a SSG-OLC-CHE | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_62 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_120 | ||
| 912 | |a GBV_ILN_2015 | ||
| 912 | |a GBV_ILN_4036 | ||
| 912 | |a GBV_ILN_4046 | ||
| 912 | |a GBV_ILN_4125 | ||
| 912 | |a GBV_ILN_4126 | ||
| 912 | |a GBV_ILN_4305 | ||
| 912 | |a GBV_ILN_4306 | ||
| 912 | |a GBV_ILN_4311 | ||
| 912 | |a GBV_ILN_4326 | ||
| 936 | r | v | |a SA 8300 |
| 951 | |a AR | ||
| 952 | |d 57 |j 2015 |e 2 |h 180 | ||
| author_variant |
y h yh |
|---|---|
| matchkey_str |
article:00401706:2015----::ttsiamtosodgaainaaihyaicvraeifrainnaapi |
| hierarchy_sort_str |
2015 |
| publishDate |
2015 |
| allfields |
10.1080/00401706.2014.915891 doi PQ20160617 (DE-627)OLC1966302592 (DE-599)GBVOLC1966302592 (PRQ)c1756-cbfc8043e824eeb91bb6ace630ccdf8a9624104483e698be94a2adc50931cec40 (KEY)0056224620150000057000200180statisticalmethodsfordegradationdatawithdynamiccov DE-627 ger DE-627 rakwb eng 600 DNB SA 8300 AVZ rvk Hong, Yili verfasserin aut Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online. Nutzungsrecht: Copyright © American Statistical Association and the American Society for Quality 2015 Organic coatings Lifetime prediction Usage history Environmental conditions Covariate process System health monitoring Algorithms Maximum likelihood method Measurement Reliability Time series Duan, Yuanyuan oth Meeker, William Q oth Stanley, Deborah L oth Gu, Xiaohong oth Enthalten in Technometrics Philadelphia, Pa. : Taylor and Francis Group, 1959 57(2015), 2, Seite 180 (DE-627)129480088 (DE-600)204242-3 (DE-576)014862395 0040-1706 nnns volume:57 year:2015 number:2 pages:180 http://dx.doi.org/10.1080/00401706.2014.915891 Volltext http://www.tandfonline.com/doi/abs/10.1080/00401706.2014.915891 http://search.proquest.com/docview/1697516931 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT GBV_ILN_20 GBV_ILN_62 GBV_ILN_70 GBV_ILN_120 GBV_ILN_2015 GBV_ILN_4036 GBV_ILN_4046 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4311 GBV_ILN_4326 SA 8300 AR 57 2015 2 180 |
| spelling |
10.1080/00401706.2014.915891 doi PQ20160617 (DE-627)OLC1966302592 (DE-599)GBVOLC1966302592 (PRQ)c1756-cbfc8043e824eeb91bb6ace630ccdf8a9624104483e698be94a2adc50931cec40 (KEY)0056224620150000057000200180statisticalmethodsfordegradationdatawithdynamiccov DE-627 ger DE-627 rakwb eng 600 DNB SA 8300 AVZ rvk Hong, Yili verfasserin aut Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online. Nutzungsrecht: Copyright © American Statistical Association and the American Society for Quality 2015 Organic coatings Lifetime prediction Usage history Environmental conditions Covariate process System health monitoring Algorithms Maximum likelihood method Measurement Reliability Time series Duan, Yuanyuan oth Meeker, William Q oth Stanley, Deborah L oth Gu, Xiaohong oth Enthalten in Technometrics Philadelphia, Pa. : Taylor and Francis Group, 1959 57(2015), 2, Seite 180 (DE-627)129480088 (DE-600)204242-3 (DE-576)014862395 0040-1706 nnns volume:57 year:2015 number:2 pages:180 http://dx.doi.org/10.1080/00401706.2014.915891 Volltext http://www.tandfonline.com/doi/abs/10.1080/00401706.2014.915891 http://search.proquest.com/docview/1697516931 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT GBV_ILN_20 GBV_ILN_62 GBV_ILN_70 GBV_ILN_120 GBV_ILN_2015 GBV_ILN_4036 GBV_ILN_4046 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4311 GBV_ILN_4326 SA 8300 AR 57 2015 2 180 |
| allfields_unstemmed |
10.1080/00401706.2014.915891 doi PQ20160617 (DE-627)OLC1966302592 (DE-599)GBVOLC1966302592 (PRQ)c1756-cbfc8043e824eeb91bb6ace630ccdf8a9624104483e698be94a2adc50931cec40 (KEY)0056224620150000057000200180statisticalmethodsfordegradationdatawithdynamiccov DE-627 ger DE-627 rakwb eng 600 DNB SA 8300 AVZ rvk Hong, Yili verfasserin aut Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online. Nutzungsrecht: Copyright © American Statistical Association and the American Society for Quality 2015 Organic coatings Lifetime prediction Usage history Environmental conditions Covariate process System health monitoring Algorithms Maximum likelihood method Measurement Reliability Time series Duan, Yuanyuan oth Meeker, William Q oth Stanley, Deborah L oth Gu, Xiaohong oth Enthalten in Technometrics Philadelphia, Pa. : Taylor and Francis Group, 1959 57(2015), 2, Seite 180 (DE-627)129480088 (DE-600)204242-3 (DE-576)014862395 0040-1706 nnns volume:57 year:2015 number:2 pages:180 http://dx.doi.org/10.1080/00401706.2014.915891 Volltext http://www.tandfonline.com/doi/abs/10.1080/00401706.2014.915891 http://search.proquest.com/docview/1697516931 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT GBV_ILN_20 GBV_ILN_62 GBV_ILN_70 GBV_ILN_120 GBV_ILN_2015 GBV_ILN_4036 GBV_ILN_4046 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4311 GBV_ILN_4326 SA 8300 AR 57 2015 2 180 |
| allfieldsGer |
10.1080/00401706.2014.915891 doi PQ20160617 (DE-627)OLC1966302592 (DE-599)GBVOLC1966302592 (PRQ)c1756-cbfc8043e824eeb91bb6ace630ccdf8a9624104483e698be94a2adc50931cec40 (KEY)0056224620150000057000200180statisticalmethodsfordegradationdatawithdynamiccov DE-627 ger DE-627 rakwb eng 600 DNB SA 8300 AVZ rvk Hong, Yili verfasserin aut Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online. Nutzungsrecht: Copyright © American Statistical Association and the American Society for Quality 2015 Organic coatings Lifetime prediction Usage history Environmental conditions Covariate process System health monitoring Algorithms Maximum likelihood method Measurement Reliability Time series Duan, Yuanyuan oth Meeker, William Q oth Stanley, Deborah L oth Gu, Xiaohong oth Enthalten in Technometrics Philadelphia, Pa. : Taylor and Francis Group, 1959 57(2015), 2, Seite 180 (DE-627)129480088 (DE-600)204242-3 (DE-576)014862395 0040-1706 nnns volume:57 year:2015 number:2 pages:180 http://dx.doi.org/10.1080/00401706.2014.915891 Volltext http://www.tandfonline.com/doi/abs/10.1080/00401706.2014.915891 http://search.proquest.com/docview/1697516931 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT GBV_ILN_20 GBV_ILN_62 GBV_ILN_70 GBV_ILN_120 GBV_ILN_2015 GBV_ILN_4036 GBV_ILN_4046 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4311 GBV_ILN_4326 SA 8300 AR 57 2015 2 180 |
| allfieldsSound |
10.1080/00401706.2014.915891 doi PQ20160617 (DE-627)OLC1966302592 (DE-599)GBVOLC1966302592 (PRQ)c1756-cbfc8043e824eeb91bb6ace630ccdf8a9624104483e698be94a2adc50931cec40 (KEY)0056224620150000057000200180statisticalmethodsfordegradationdatawithdynamiccov DE-627 ger DE-627 rakwb eng 600 DNB SA 8300 AVZ rvk Hong, Yili verfasserin aut Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online. Nutzungsrecht: Copyright © American Statistical Association and the American Society for Quality 2015 Organic coatings Lifetime prediction Usage history Environmental conditions Covariate process System health monitoring Algorithms Maximum likelihood method Measurement Reliability Time series Duan, Yuanyuan oth Meeker, William Q oth Stanley, Deborah L oth Gu, Xiaohong oth Enthalten in Technometrics Philadelphia, Pa. : Taylor and Francis Group, 1959 57(2015), 2, Seite 180 (DE-627)129480088 (DE-600)204242-3 (DE-576)014862395 0040-1706 nnns volume:57 year:2015 number:2 pages:180 http://dx.doi.org/10.1080/00401706.2014.915891 Volltext http://www.tandfonline.com/doi/abs/10.1080/00401706.2014.915891 http://search.proquest.com/docview/1697516931 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT GBV_ILN_20 GBV_ILN_62 GBV_ILN_70 GBV_ILN_120 GBV_ILN_2015 GBV_ILN_4036 GBV_ILN_4046 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4311 GBV_ILN_4326 SA 8300 AR 57 2015 2 180 |
| language |
English |
| source |
Enthalten in Technometrics 57(2015), 2, Seite 180 volume:57 year:2015 number:2 pages:180 |
| sourceStr |
Enthalten in Technometrics 57(2015), 2, Seite 180 volume:57 year:2015 number:2 pages:180 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Organic coatings Lifetime prediction Usage history Environmental conditions Covariate process System health monitoring Algorithms Maximum likelihood method Measurement Reliability Time series |
| dewey-raw |
600 |
| isfreeaccess_bool |
false |
| container_title |
Technometrics |
| authorswithroles_txt_mv |
Hong, Yili @@aut@@ Duan, Yuanyuan @@oth@@ Meeker, William Q @@oth@@ Stanley, Deborah L @@oth@@ Gu, Xiaohong @@oth@@ |
| publishDateDaySort_date |
2015-01-01T00:00:00Z |
| hierarchy_top_id |
129480088 |
| dewey-sort |
3600 |
| id |
OLC1966302592 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1966302592</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230714165219.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160206s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/00401706.2014.915891</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160617</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1966302592</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1966302592</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c1756-cbfc8043e824eeb91bb6ace630ccdf8a9624104483e698be94a2adc50931cec40</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0056224620150000057000200180statisticalmethodsfordegradationdatawithdynamiccov</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">600</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SA 8300</subfield><subfield code="q">AVZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Hong, Yili</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</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="520" ind1=" " ind2=" "><subfield code="a">Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: Copyright © American Statistical Association and the American Society for Quality 2015</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Organic coatings</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Lifetime prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Usage history</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Environmental conditions</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Covariate process</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">System health monitoring</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Maximum likelihood method</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Measurement</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Reliability</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Time series</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Duan, Yuanyuan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Meeker, William Q</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Stanley, Deborah L</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gu, Xiaohong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Technometrics</subfield><subfield code="d">Philadelphia, Pa. : Taylor and Francis Group, 1959</subfield><subfield code="g">57(2015), 2, Seite 180</subfield><subfield code="w">(DE-627)129480088</subfield><subfield code="w">(DE-600)204242-3</subfield><subfield code="w">(DE-576)014862395</subfield><subfield code="x">0040-1706</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:57</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:2</subfield><subfield code="g">pages:180</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1080/00401706.2014.915891</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.tandfonline.com/doi/abs/10.1080/00401706.2014.915891</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1697516931</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-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-CHE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4036</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4311</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">SA 8300</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">2015</subfield><subfield code="e">2</subfield><subfield code="h">180</subfield></datafield></record></collection>
|
| author |
Hong, Yili |
| spellingShingle |
Hong, Yili ddc 600 rvk SA 8300 misc Organic coatings misc Lifetime prediction misc Usage history misc Environmental conditions misc Covariate process misc System health monitoring misc Algorithms misc Maximum likelihood method misc Measurement misc Reliability misc Time series Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data |
| authorStr |
Hong, Yili |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)129480088 |
| format |
Article |
| dewey-ones |
600 - Technology |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0040-1706 |
| topic_title |
600 DNB SA 8300 AVZ rvk Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data Organic coatings Lifetime prediction Usage history Environmental conditions Covariate process System health monitoring Algorithms Maximum likelihood method Measurement Reliability Time series |
| topic |
ddc 600 rvk SA 8300 misc Organic coatings misc Lifetime prediction misc Usage history misc Environmental conditions misc Covariate process misc System health monitoring misc Algorithms misc Maximum likelihood method misc Measurement misc Reliability misc Time series |
| topic_unstemmed |
ddc 600 rvk SA 8300 misc Organic coatings misc Lifetime prediction misc Usage history misc Environmental conditions misc Covariate process misc System health monitoring misc Algorithms misc Maximum likelihood method misc Measurement misc Reliability misc Time series |
| topic_browse |
ddc 600 rvk SA 8300 misc Organic coatings misc Lifetime prediction misc Usage history misc Environmental conditions misc Covariate process misc System health monitoring misc Algorithms misc Maximum likelihood method misc Measurement misc Reliability misc Time series |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| author2_variant |
y d yd w q m wq wqm d l s dl dls x g xg |
| hierarchy_parent_title |
Technometrics |
| hierarchy_parent_id |
129480088 |
| dewey-tens |
600 - Technology |
| hierarchy_top_title |
Technometrics |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)129480088 (DE-600)204242-3 (DE-576)014862395 |
| title |
Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data |
| ctrlnum |
(DE-627)OLC1966302592 (DE-599)GBVOLC1966302592 (PRQ)c1756-cbfc8043e824eeb91bb6ace630ccdf8a9624104483e698be94a2adc50931cec40 (KEY)0056224620150000057000200180statisticalmethodsfordegradationdatawithdynamiccov |
| title_full |
Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data |
| author_sort |
Hong, Yili |
| journal |
Technometrics |
| journalStr |
Technometrics |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2015 |
| contenttype_str_mv |
txt |
| container_start_page |
180 |
| author_browse |
Hong, Yili |
| container_volume |
57 |
| class |
600 DNB SA 8300 AVZ rvk |
| format_se |
Aufsätze |
| author-letter |
Hong, Yili |
| doi_str_mv |
10.1080/00401706.2014.915891 |
| dewey-full |
600 |
| title_sort |
statistical methods for degradation data with dynamic covariates information and an application to outdoor weathering data |
| title_auth |
Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data |
| abstract |
Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online. |
| abstractGer |
Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online. |
| abstract_unstemmed |
Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT GBV_ILN_20 GBV_ILN_62 GBV_ILN_70 GBV_ILN_120 GBV_ILN_2015 GBV_ILN_4036 GBV_ILN_4046 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4311 GBV_ILN_4326 |
| container_issue |
2 |
| title_short |
Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data |
| url |
http://dx.doi.org/10.1080/00401706.2014.915891 http://www.tandfonline.com/doi/abs/10.1080/00401706.2014.915891 http://search.proquest.com/docview/1697516931 |
| remote_bool |
false |
| author2 |
Duan, Yuanyuan Meeker, William Q Stanley, Deborah L Gu, Xiaohong |
| author2Str |
Duan, Yuanyuan Meeker, William Q Stanley, Deborah L Gu, Xiaohong |
| ppnlink |
129480088 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth |
| doi_str |
10.1080/00401706.2014.915891 |
| up_date |
2024-07-03T21:08:24.743Z |
| _version_ |
1803593611733893120 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1966302592</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230714165219.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160206s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/00401706.2014.915891</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160617</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1966302592</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1966302592</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c1756-cbfc8043e824eeb91bb6ace630ccdf8a9624104483e698be94a2adc50931cec40</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0056224620150000057000200180statisticalmethodsfordegradationdatawithdynamiccov</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">600</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SA 8300</subfield><subfield code="q">AVZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Hong, Yili</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</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="520" ind1=" " ind2=" "><subfield code="a">Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: Copyright © American Statistical Association and the American Society for Quality 2015</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Organic coatings</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Lifetime prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Usage history</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Environmental conditions</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Covariate process</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">System health monitoring</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Maximum likelihood method</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Measurement</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Reliability</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Time series</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Duan, Yuanyuan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Meeker, William Q</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Stanley, Deborah L</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gu, Xiaohong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Technometrics</subfield><subfield code="d">Philadelphia, Pa. : Taylor and Francis Group, 1959</subfield><subfield code="g">57(2015), 2, Seite 180</subfield><subfield code="w">(DE-627)129480088</subfield><subfield code="w">(DE-600)204242-3</subfield><subfield code="w">(DE-576)014862395</subfield><subfield code="x">0040-1706</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:57</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:2</subfield><subfield code="g">pages:180</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1080/00401706.2014.915891</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.tandfonline.com/doi/abs/10.1080/00401706.2014.915891</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1697516931</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-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-CHE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4036</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4311</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">SA 8300</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">2015</subfield><subfield code="e">2</subfield><subfield code="h">180</subfield></datafield></record></collection>
|
| score |
7.3972025 |