A comparative study of observation-error estimators and state-space production models in fisheries assessment and management
State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compar...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Xu, Luoliang [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Correction - 2017, an international journal on fisheries science, fishing technology and fisheries management, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:219 ; year:2019 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.fishres.2019.105322 |
|---|
| Katalog-ID: |
ELV047847379 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV047847379 | ||
| 003 | DE-627 | ||
| 005 | 20230626020625.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 191023s2019 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.fishres.2019.105322 |2 doi | |
| 028 | 5 | 2 | |a GBV00000000000769.pica |
| 035 | |a (DE-627)ELV047847379 | ||
| 035 | |a (ELSEVIER)S0165-7836(19)30169-9 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 610 |q VZ |
| 082 | 0 | 4 | |a 610 |q VZ |
| 084 | |a 44.85 |2 bkl | ||
| 100 | 1 | |a Xu, Luoliang |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a A comparative study of observation-error estimators and state-space production models in fisheries assessment and management |
| 264 | 1 | |c 2019transfer abstract | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management. | ||
| 520 | |a State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management. | ||
| 650 | 7 | |a Production models |2 Elsevier | |
| 650 | 7 | |a Model errors |2 Elsevier | |
| 650 | 7 | |a Process errors |2 Elsevier | |
| 650 | 7 | |a Observation errors |2 Elsevier | |
| 650 | 7 | |a Uncertainties |2 Elsevier | |
| 650 | 7 | |a State-space models |2 Elsevier | |
| 700 | 1 | |a Li, Bai |4 oth | |
| 700 | 1 | |a Chen, Xinjun |4 oth | |
| 700 | 1 | |a Chen, Yong |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |t Correction |d 2017 |d an international journal on fisheries science, fishing technology and fisheries management |g Amsterdam [u.a.] |w (DE-627)ELV014719592 |
| 773 | 1 | 8 | |g volume:219 |g year:2019 |g pages:0 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.fishres.2019.105322 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 912 | |a GBV_ILN_40 | ||
| 936 | b | k | |a 44.85 |j Kardiologie |j Angiologie |q VZ |
| 951 | |a AR | ||
| 952 | |d 219 |j 2019 |h 0 | ||
| author_variant |
l x lx |
|---|---|
| matchkey_str |
xuluolianglibaichenxinjunchenyong:2019----:cmaaietdoosrainroetmtradttsaerdcinoesnih |
| hierarchy_sort_str |
2019transfer abstract |
| bklnumber |
44.85 |
| publishDate |
2019 |
| allfields |
10.1016/j.fishres.2019.105322 doi GBV00000000000769.pica (DE-627)ELV047847379 (ELSEVIER)S0165-7836(19)30169-9 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.85 bkl Xu, Luoliang verfasserin aut A comparative study of observation-error estimators and state-space production models in fisheries assessment and management 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management. State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management. Production models Elsevier Model errors Elsevier Process errors Elsevier Observation errors Elsevier Uncertainties Elsevier State-space models Elsevier Li, Bai oth Chen, Xinjun oth Chen, Yong oth Enthalten in Elsevier Science Correction 2017 an international journal on fisheries science, fishing technology and fisheries management Amsterdam [u.a.] (DE-627)ELV014719592 volume:219 year:2019 pages:0 https://doi.org/10.1016/j.fishres.2019.105322 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 44.85 Kardiologie Angiologie VZ AR 219 2019 0 |
| spelling |
10.1016/j.fishres.2019.105322 doi GBV00000000000769.pica (DE-627)ELV047847379 (ELSEVIER)S0165-7836(19)30169-9 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.85 bkl Xu, Luoliang verfasserin aut A comparative study of observation-error estimators and state-space production models in fisheries assessment and management 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management. State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management. Production models Elsevier Model errors Elsevier Process errors Elsevier Observation errors Elsevier Uncertainties Elsevier State-space models Elsevier Li, Bai oth Chen, Xinjun oth Chen, Yong oth Enthalten in Elsevier Science Correction 2017 an international journal on fisheries science, fishing technology and fisheries management Amsterdam [u.a.] (DE-627)ELV014719592 volume:219 year:2019 pages:0 https://doi.org/10.1016/j.fishres.2019.105322 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 44.85 Kardiologie Angiologie VZ AR 219 2019 0 |
| allfields_unstemmed |
10.1016/j.fishres.2019.105322 doi GBV00000000000769.pica (DE-627)ELV047847379 (ELSEVIER)S0165-7836(19)30169-9 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.85 bkl Xu, Luoliang verfasserin aut A comparative study of observation-error estimators and state-space production models in fisheries assessment and management 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management. State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management. Production models Elsevier Model errors Elsevier Process errors Elsevier Observation errors Elsevier Uncertainties Elsevier State-space models Elsevier Li, Bai oth Chen, Xinjun oth Chen, Yong oth Enthalten in Elsevier Science Correction 2017 an international journal on fisheries science, fishing technology and fisheries management Amsterdam [u.a.] (DE-627)ELV014719592 volume:219 year:2019 pages:0 https://doi.org/10.1016/j.fishres.2019.105322 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 44.85 Kardiologie Angiologie VZ AR 219 2019 0 |
| allfieldsGer |
10.1016/j.fishres.2019.105322 doi GBV00000000000769.pica (DE-627)ELV047847379 (ELSEVIER)S0165-7836(19)30169-9 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.85 bkl Xu, Luoliang verfasserin aut A comparative study of observation-error estimators and state-space production models in fisheries assessment and management 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management. State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management. Production models Elsevier Model errors Elsevier Process errors Elsevier Observation errors Elsevier Uncertainties Elsevier State-space models Elsevier Li, Bai oth Chen, Xinjun oth Chen, Yong oth Enthalten in Elsevier Science Correction 2017 an international journal on fisheries science, fishing technology and fisheries management Amsterdam [u.a.] (DE-627)ELV014719592 volume:219 year:2019 pages:0 https://doi.org/10.1016/j.fishres.2019.105322 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 44.85 Kardiologie Angiologie VZ AR 219 2019 0 |
| allfieldsSound |
10.1016/j.fishres.2019.105322 doi GBV00000000000769.pica (DE-627)ELV047847379 (ELSEVIER)S0165-7836(19)30169-9 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.85 bkl Xu, Luoliang verfasserin aut A comparative study of observation-error estimators and state-space production models in fisheries assessment and management 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management. State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management. Production models Elsevier Model errors Elsevier Process errors Elsevier Observation errors Elsevier Uncertainties Elsevier State-space models Elsevier Li, Bai oth Chen, Xinjun oth Chen, Yong oth Enthalten in Elsevier Science Correction 2017 an international journal on fisheries science, fishing technology and fisheries management Amsterdam [u.a.] (DE-627)ELV014719592 volume:219 year:2019 pages:0 https://doi.org/10.1016/j.fishres.2019.105322 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 44.85 Kardiologie Angiologie VZ AR 219 2019 0 |
| language |
English |
| source |
Enthalten in Correction Amsterdam [u.a.] volume:219 year:2019 pages:0 |
| sourceStr |
Enthalten in Correction Amsterdam [u.a.] volume:219 year:2019 pages:0 |
| format_phy_str_mv |
Article |
| bklname |
Kardiologie Angiologie |
| institution |
findex.gbv.de |
| topic_facet |
Production models Model errors Process errors Observation errors Uncertainties State-space models |
| dewey-raw |
610 |
| isfreeaccess_bool |
false |
| container_title |
Correction |
| authorswithroles_txt_mv |
Xu, Luoliang @@aut@@ Li, Bai @@oth@@ Chen, Xinjun @@oth@@ Chen, Yong @@oth@@ |
| publishDateDaySort_date |
2019-01-01T00:00:00Z |
| hierarchy_top_id |
ELV014719592 |
| dewey-sort |
3610 |
| id |
ELV047847379 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV047847379</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626020625.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191023s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.fishres.2019.105322</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000769.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV047847379</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0165-7836(19)30169-9</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">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.85</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xu, Luoliang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A comparative study of observation-error estimators and state-space production models in fisheries assessment and management</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Production models</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Model errors</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Process errors</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Observation errors</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Uncertainties</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">State-space models</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Bai</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Xinjun</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Yong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="t">Correction</subfield><subfield code="d">2017</subfield><subfield code="d">an international journal on fisheries science, fishing technology and fisheries management</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV014719592</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:219</subfield><subfield code="g">year:2019</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.fishres.2019.105322</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.85</subfield><subfield code="j">Kardiologie</subfield><subfield code="j">Angiologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">219</subfield><subfield code="j">2019</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| author |
Xu, Luoliang |
| spellingShingle |
Xu, Luoliang ddc 610 bkl 44.85 Elsevier Production models Elsevier Model errors Elsevier Process errors Elsevier Observation errors Elsevier Uncertainties Elsevier State-space models A comparative study of observation-error estimators and state-space production models in fisheries assessment and management |
| authorStr |
Xu, Luoliang |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV014719592 |
| format |
electronic Article |
| dewey-ones |
610 - Medicine & health |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
610 VZ 44.85 bkl A comparative study of observation-error estimators and state-space production models in fisheries assessment and management Production models Elsevier Model errors Elsevier Process errors Elsevier Observation errors Elsevier Uncertainties Elsevier State-space models Elsevier |
| topic |
ddc 610 bkl 44.85 Elsevier Production models Elsevier Model errors Elsevier Process errors Elsevier Observation errors Elsevier Uncertainties Elsevier State-space models |
| topic_unstemmed |
ddc 610 bkl 44.85 Elsevier Production models Elsevier Model errors Elsevier Process errors Elsevier Observation errors Elsevier Uncertainties Elsevier State-space models |
| topic_browse |
ddc 610 bkl 44.85 Elsevier Production models Elsevier Model errors Elsevier Process errors Elsevier Observation errors Elsevier Uncertainties Elsevier State-space models |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
b l bl x c xc y c yc |
| hierarchy_parent_title |
Correction |
| hierarchy_parent_id |
ELV014719592 |
| dewey-tens |
610 - Medicine & health |
| hierarchy_top_title |
Correction |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV014719592 |
| title |
A comparative study of observation-error estimators and state-space production models in fisheries assessment and management |
| ctrlnum |
(DE-627)ELV047847379 (ELSEVIER)S0165-7836(19)30169-9 |
| title_full |
A comparative study of observation-error estimators and state-space production models in fisheries assessment and management |
| author_sort |
Xu, Luoliang |
| journal |
Correction |
| journalStr |
Correction |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2019 |
| contenttype_str_mv |
zzz |
| container_start_page |
0 |
| author_browse |
Xu, Luoliang |
| container_volume |
219 |
| class |
610 VZ 44.85 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Xu, Luoliang |
| doi_str_mv |
10.1016/j.fishres.2019.105322 |
| dewey-full |
610 |
| title_sort |
a comparative study of observation-error estimators and state-space production models in fisheries assessment and management |
| title_auth |
A comparative study of observation-error estimators and state-space production models in fisheries assessment and management |
| abstract |
State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management. |
| abstractGer |
State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management. |
| abstract_unstemmed |
State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 |
| title_short |
A comparative study of observation-error estimators and state-space production models in fisheries assessment and management |
| url |
https://doi.org/10.1016/j.fishres.2019.105322 |
| remote_bool |
true |
| author2 |
Li, Bai Chen, Xinjun Chen, Yong |
| author2Str |
Li, Bai Chen, Xinjun Chen, Yong |
| ppnlink |
ELV014719592 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth |
| doi_str |
10.1016/j.fishres.2019.105322 |
| up_date |
2024-07-06T17:16:48.203Z |
| _version_ |
1803850831054766080 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV047847379</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626020625.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191023s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.fishres.2019.105322</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000769.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV047847379</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0165-7836(19)30169-9</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">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.85</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xu, Luoliang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A comparative study of observation-error estimators and state-space production models in fisheries assessment and management</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">State-space production models are increasingly being used in fisheries stock assessment as they provide the ability to account for observation and process errors. However, model performance when the population dynamics specified differs from the true biological process requires evaluation. We compared the estimation performance of a standard observation-error approach with a state-space production model for various simulated levels of model, process, and observation errors. We found that the state-space production model was generally superior to the observation-error estimator. However, the advantage of the state-space production model in parameter estimation diminished with increased model errors. The observation-error estimator outperformed the state-space production model when model error exceeded a certain level. A significant number of small process and observation error estimates (<0.0001) from the state-space model were observed. The process and observation error estimates were biased, with the bias direction influenced by the ratio of process error to observation error. Our results highlight precautions in applying different types of production model estimators in fisheries stock assessment and management.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Production models</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Model errors</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Process errors</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Observation errors</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Uncertainties</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">State-space models</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Bai</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Xinjun</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Yong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="t">Correction</subfield><subfield code="d">2017</subfield><subfield code="d">an international journal on fisheries science, fishing technology and fisheries management</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV014719592</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:219</subfield><subfield code="g">year:2019</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.fishres.2019.105322</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.85</subfield><subfield code="j">Kardiologie</subfield><subfield code="j">Angiologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">219</subfield><subfield code="j">2019</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| score |
7.3997 |