Classical and Bayesian estimation for type-I extended-F family with an actuarial application
In this work, a new flexible class, called the type-I extended-F family, is proposed. A special sub-model of the proposed class, called type-I extended-Weibull (TIEx-W) distribution, is explored in detail. Basic properties of the TIEx-W distribution are provided. The parameters of the TIEx-W distrib...
Gespeichert in:
| Veröffentlicht in: | PloS one 2023-02, Vol.18 (2), p.e0275430 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 2 |
| container_start_page | e0275430 |
| container_title | PloS one |
| container_volume | 18 |
| creator | Alfaer, Nada M Bandar, Sarah A Kharazmi, Omid Al-Mofleh, Hazem Ahmad, Zubair Afify, Ahmed Z |
| description | In this work, a new flexible class, called the type-I extended-F family, is proposed. A special sub-model of the proposed class, called type-I extended-Weibull (TIEx-W) distribution, is explored in detail. Basic properties of the TIEx-W distribution are provided. The parameters of the TIEx-W distribution are obtained by eight classical methods of estimation. The performance of these estimators is explored using Monte Carlo simulation results for small and large samples. Besides, the Bayesian estimation of the model parameters under different loss functions for the real data set is also provided. The importance and flexibility of the TIEx-W model are illustrated by analyzing an insurance data. The real-life insurance data illustrates that the TIEx-W distribution provides better fit as compared to competing models such as Lindley-Weibull, exponentiated Weibull, Kumaraswamy-Weibull, α logarithmic transformed Weibull, and beta Weibull distributions, among others. |
| doi_str_mv | 10.1371/journal.pone.0275430 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2771911135</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A735606377</galeid><doaj_id>oai_doaj_org_article_c325638c708248d89c8a324b4a601a23</doaj_id><sourcerecordid>A735606377</sourcerecordid><originalsourceid>FETCH-LOGICAL-c641t-5f8862d4a83f66f4cdc3289fba10541ceb8b85fba018515266dd0a540fcd3a5e3</originalsourceid><addsrcrecordid>eNqNk12L1DAUhoso7rr6D0QLguhFx6T5aHojrIOrAwsLfl0J4UySzmTJNDVp1fn3pk53mcpeSC_aJM_7np6Tc7LsKUYLTCr85toPoQW36HxrFqisGCXoXnaKa1IWvETk_tH3SfYoxmuEGBGcP8xOCK8IIgidZt-XDmK0ClwOrc7fwd5EC21uYm930Fvf5o0Peb_vTLHKze_etNro4iJvYGfdPv9l-21S5qD6AYIdbbrOJb9R-jh70ICL5sn0Psu-Xrz_svxYXF59WC3PLwvFKe4L1gjBS01BkIbzhiqtSCnqZg0YMYqVWYu1YGmJsGCYlZxrjYBR1ChNgBlylj0_-HbORzkVJsqyqnCNMSYsEasDoT1cyy6k3MJeerDy74YPGwmht8oZmWIzToSqkCip0KJWAkhJ1xQ4wlCS5PV2ijasd0Yr0_YB3Mx0ftLardz4n7IWNaVcJINXk0HwP4ZUabmzURnnoDV-mP6bUsFpQl_8g96d3URtICVg28anuGo0lecVYRxxUlWJWtxBpUebnVWpixqb9meC1zNBYvrUARsYYpSrz5_-n736NmdfHrFbA67fRu-GsWXiHKQHUAUfYzDNbZExkuMQ3FRDjkMgpyFIsmfHF3Qruul68gfNIwDK</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2771911135</pqid></control><display><type>article</type><title>Classical and Bayesian estimation for type-I extended-F family with an actuarial application</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Public Library of Science (PLoS)</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Alfaer, Nada M ; Bandar, Sarah A ; Kharazmi, Omid ; Al-Mofleh, Hazem ; Ahmad, Zubair ; Afify, Ahmed Z</creator><contributor>Gadde, Srinivasa Rao</contributor><creatorcontrib>Alfaer, Nada M ; Bandar, Sarah A ; Kharazmi, Omid ; Al-Mofleh, Hazem ; Ahmad, Zubair ; Afify, Ahmed Z ; Gadde, Srinivasa Rao</creatorcontrib><description>In this work, a new flexible class, called the type-I extended-F family, is proposed. A special sub-model of the proposed class, called type-I extended-Weibull (TIEx-W) distribution, is explored in detail. Basic properties of the TIEx-W distribution are provided. The parameters of the TIEx-W distribution are obtained by eight classical methods of estimation. The performance of these estimators is explored using Monte Carlo simulation results for small and large samples. Besides, the Bayesian estimation of the model parameters under different loss functions for the real data set is also provided. The importance and flexibility of the TIEx-W model are illustrated by analyzing an insurance data. The real-life insurance data illustrates that the TIEx-W distribution provides better fit as compared to competing models such as Lindley-Weibull, exponentiated Weibull, Kumaraswamy-Weibull, α logarithmic transformed Weibull, and beta Weibull distributions, among others.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0275430</identifier><identifier>PMID: 36730300</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Bayes Theorem ; Bayesian analysis ; Bayesian statistical decision theory ; Computer Simulation ; Engineering and Technology ; Estimates ; Evaluation ; Information management ; Insurance ; Likelihood Functions ; Mathematical models ; Maximum likelihood method ; Medicine and Health Sciences ; Modelling ; Monte Carlo Method ; Monte Carlo simulation ; Parameter estimation ; Parameters ; People and places ; Physical Sciences ; Random variables ; Research and Analysis Methods ; Social Sciences ; Statistical Distributions ; Statistics ; Weibull distribution</subject><ispartof>PloS one, 2023-02, Vol.18 (2), p.e0275430</ispartof><rights>Copyright: © 2023 Alfaer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Alfaer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Alfaer et al 2023 Alfaer et al</rights><rights>2023 Alfaer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c641t-5f8862d4a83f66f4cdc3289fba10541ceb8b85fba018515266dd0a540fcd3a5e3</cites><orcidid>0000-0002-6723-6785 ; 0000-0003-3430-2464</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894468/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894468/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36730300$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Gadde, Srinivasa Rao</contributor><creatorcontrib>Alfaer, Nada M</creatorcontrib><creatorcontrib>Bandar, Sarah A</creatorcontrib><creatorcontrib>Kharazmi, Omid</creatorcontrib><creatorcontrib>Al-Mofleh, Hazem</creatorcontrib><creatorcontrib>Ahmad, Zubair</creatorcontrib><creatorcontrib>Afify, Ahmed Z</creatorcontrib><title>Classical and Bayesian estimation for type-I extended-F family with an actuarial application</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>In this work, a new flexible class, called the type-I extended-F family, is proposed. A special sub-model of the proposed class, called type-I extended-Weibull (TIEx-W) distribution, is explored in detail. Basic properties of the TIEx-W distribution are provided. The parameters of the TIEx-W distribution are obtained by eight classical methods of estimation. The performance of these estimators is explored using Monte Carlo simulation results for small and large samples. Besides, the Bayesian estimation of the model parameters under different loss functions for the real data set is also provided. The importance and flexibility of the TIEx-W model are illustrated by analyzing an insurance data. The real-life insurance data illustrates that the TIEx-W distribution provides better fit as compared to competing models such as Lindley-Weibull, exponentiated Weibull, Kumaraswamy-Weibull, α logarithmic transformed Weibull, and beta Weibull distributions, among others.</description><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Bayesian statistical decision theory</subject><subject>Computer Simulation</subject><subject>Engineering and Technology</subject><subject>Estimates</subject><subject>Evaluation</subject><subject>Information management</subject><subject>Insurance</subject><subject>Likelihood Functions</subject><subject>Mathematical models</subject><subject>Maximum likelihood method</subject><subject>Medicine and Health Sciences</subject><subject>Modelling</subject><subject>Monte Carlo Method</subject><subject>Monte Carlo simulation</subject><subject>Parameter estimation</subject><subject>Parameters</subject><subject>People and places</subject><subject>Physical Sciences</subject><subject>Random variables</subject><subject>Research and Analysis Methods</subject><subject>Social Sciences</subject><subject>Statistical Distributions</subject><subject>Statistics</subject><subject>Weibull distribution</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk12L1DAUhoso7rr6D0QLguhFx6T5aHojrIOrAwsLfl0J4UySzmTJNDVp1fn3pk53mcpeSC_aJM_7np6Tc7LsKUYLTCr85toPoQW36HxrFqisGCXoXnaKa1IWvETk_tH3SfYoxmuEGBGcP8xOCK8IIgidZt-XDmK0ClwOrc7fwd5EC21uYm930Fvf5o0Peb_vTLHKze_etNro4iJvYGfdPv9l-21S5qD6AYIdbbrOJb9R-jh70ICL5sn0Psu-Xrz_svxYXF59WC3PLwvFKe4L1gjBS01BkIbzhiqtSCnqZg0YMYqVWYu1YGmJsGCYlZxrjYBR1ChNgBlylj0_-HbORzkVJsqyqnCNMSYsEasDoT1cyy6k3MJeerDy74YPGwmht8oZmWIzToSqkCip0KJWAkhJ1xQ4wlCS5PV2ijasd0Yr0_YB3Mx0ftLardz4n7IWNaVcJINXk0HwP4ZUabmzURnnoDV-mP6bUsFpQl_8g96d3URtICVg28anuGo0lecVYRxxUlWJWtxBpUebnVWpixqb9meC1zNBYvrUARsYYpSrz5_-n736NmdfHrFbA67fRu-GsWXiHKQHUAUfYzDNbZExkuMQ3FRDjkMgpyFIsmfHF3Qruul68gfNIwDK</recordid><startdate>20230202</startdate><enddate>20230202</enddate><creator>Alfaer, Nada M</creator><creator>Bandar, Sarah A</creator><creator>Kharazmi, Omid</creator><creator>Al-Mofleh, Hazem</creator><creator>Ahmad, Zubair</creator><creator>Afify, Ahmed Z</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>COVID</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-6723-6785</orcidid><orcidid>https://orcid.org/0000-0003-3430-2464</orcidid></search><sort><creationdate>20230202</creationdate><title>Classical and Bayesian estimation for type-I extended-F family with an actuarial application</title><author>Alfaer, Nada M ; Bandar, Sarah A ; Kharazmi, Omid ; Al-Mofleh, Hazem ; Ahmad, Zubair ; Afify, Ahmed Z</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c641t-5f8862d4a83f66f4cdc3289fba10541ceb8b85fba018515266dd0a540fcd3a5e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Bayesian statistical decision theory</topic><topic>Computer Simulation</topic><topic>Engineering and Technology</topic><topic>Estimates</topic><topic>Evaluation</topic><topic>Information management</topic><topic>Insurance</topic><topic>Likelihood Functions</topic><topic>Mathematical models</topic><topic>Maximum likelihood method</topic><topic>Medicine and Health Sciences</topic><topic>Modelling</topic><topic>Monte Carlo Method</topic><topic>Monte Carlo simulation</topic><topic>Parameter estimation</topic><topic>Parameters</topic><topic>People and places</topic><topic>Physical Sciences</topic><topic>Random variables</topic><topic>Research and Analysis Methods</topic><topic>Social Sciences</topic><topic>Statistical Distributions</topic><topic>Statistics</topic><topic>Weibull distribution</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Alfaer, Nada M</creatorcontrib><creatorcontrib>Bandar, Sarah A</creatorcontrib><creatorcontrib>Kharazmi, Omid</creatorcontrib><creatorcontrib>Al-Mofleh, Hazem</creatorcontrib><creatorcontrib>Ahmad, Zubair</creatorcontrib><creatorcontrib>Afify, Ahmed Z</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alfaer, Nada M</au><au>Bandar, Sarah A</au><au>Kharazmi, Omid</au><au>Al-Mofleh, Hazem</au><au>Ahmad, Zubair</au><au>Afify, Ahmed Z</au><au>Gadde, Srinivasa Rao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Classical and Bayesian estimation for type-I extended-F family with an actuarial application</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2023-02-02</date><risdate>2023</risdate><volume>18</volume><issue>2</issue><spage>e0275430</spage><pages>e0275430-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>In this work, a new flexible class, called the type-I extended-F family, is proposed. A special sub-model of the proposed class, called type-I extended-Weibull (TIEx-W) distribution, is explored in detail. Basic properties of the TIEx-W distribution are provided. The parameters of the TIEx-W distribution are obtained by eight classical methods of estimation. The performance of these estimators is explored using Monte Carlo simulation results for small and large samples. Besides, the Bayesian estimation of the model parameters under different loss functions for the real data set is also provided. The importance and flexibility of the TIEx-W model are illustrated by analyzing an insurance data. The real-life insurance data illustrates that the TIEx-W distribution provides better fit as compared to competing models such as Lindley-Weibull, exponentiated Weibull, Kumaraswamy-Weibull, α logarithmic transformed Weibull, and beta Weibull distributions, among others.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>36730300</pmid><doi>10.1371/journal.pone.0275430</doi><tpages>e0275430</tpages><orcidid>https://orcid.org/0000-0002-6723-6785</orcidid><orcidid>https://orcid.org/0000-0003-3430-2464</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2023-02, Vol.18 (2), p.e0275430 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2771911135 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Bayes Theorem Bayesian analysis Bayesian statistical decision theory Computer Simulation Engineering and Technology Estimates Evaluation Information management Insurance Likelihood Functions Mathematical models Maximum likelihood method Medicine and Health Sciences Modelling Monte Carlo Method Monte Carlo simulation Parameter estimation Parameters People and places Physical Sciences Random variables Research and Analysis Methods Social Sciences Statistical Distributions Statistics Weibull distribution |
| title | Classical and Bayesian estimation for type-I extended-F family with an actuarial application |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-19T09%3A24%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Classical%20and%20Bayesian%20estimation%20for%20type-I%20extended-F%20family%20with%20an%20actuarial%20application&rft.jtitle=PloS%20one&rft.au=Alfaer,%20Nada%20M&rft.date=2023-02-02&rft.volume=18&rft.issue=2&rft.spage=e0275430&rft.pages=e0275430-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0275430&rft_dat=%3Cgale_plos_%3EA735606377%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2771911135&rft_id=info:pmid/36730300&rft_galeid=A735606377&rft_doaj_id=oai_doaj_org_article_c325638c708248d89c8a324b4a601a23&rfr_iscdi=true |