On Discriminating between Gamma and Log-logistic Distributions in Case of Progressive Type II Censoring
Gamma and log-logistic distributions are two popular distributions for analyzing lifetime data. In this paper, the problem of discriminating between these two distribution functions is considered in case of progressive type II censoring. The ratio of the maximized likelihood test (RML) is used to di...
Gespeichert in:
Veröffentlicht in: | Pakistan journal of statistics and operation research 2017-01, Vol.13 (1), p.157 |
---|---|
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 | 1 |
container_start_page | 157 |
container_title | Pakistan journal of statistics and operation research |
container_volume | 13 |
creator | Ahmed Elsherpieny, Elsayed Zeyada Muhammed, Hiba Usama Mohamed Mohamed Radwan, Noha |
description | Gamma and log-logistic distributions are two popular distributions for analyzing lifetime data. In this paper, the problem of discriminating between these two distribution functions is considered in case of progressive type II censoring. The ratio of the maximized likelihood test (RML) is used to discriminate between them. Some simulation experiments were performed to see how the probability of correct selection (PCS) under each model work for small sample sizes. Real data life is analyzed to see how the proposed method works in practice. As a special case of progressive type II censoring, the problem of discriminating between gamma and log-logistic in case of complete samples is considered. The RML and the ratio of Minimized Kullback-Leibler Divergence (RMKLD) tests are used to discriminate between them. The asymptotic results are used to estimate the PCS which is used to calculate the minimum sample size required for discriminating between two distributions. Two real life data are analyzed. |
doi_str_mv | 10.18187/pjsor.v13i1.1524 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_1903496994</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1903496994</sourcerecordid><originalsourceid>FETCH-LOGICAL-c273t-6ea1ab9365a971a01cb90616a893e7dd1f5af9754b8cc8f72b7689ba39d841db3</originalsourceid><addsrcrecordid>eNotkM1KAzEURoMoWLQP4C7geurcZCY_S6laC4W6qOuQzGSGlDYZk2mlb2_aurqbw7kfB6EnKGcgQPCXYZtCnB2BOphBTaobNCGElEUtoLxFkwyxgnCAezRNyZmSMMkzxyeoX3v85lIT3d55PTrfY2PHX2s9Xuj9XmPtW7wKfbELvUuja870GJ05jC74hJ3Hc50sDh3-iqGPNvuPFm9Og8XLJZ5bn5dl6yO66_Qu2en_fUDfH--b-WexWi-W89dV0RBOx4JZDdpIymqdF-oSGiNLBkwLSS1vW-hq3UleV0Y0jeg4MZwJaTSVraigNfQBPV-9Qww_B5tGtQ2H6PNLBbKklWRSVpmCK9XEkFK0nRpyAB1PCkp1SaouSdUlqTonpX96iWxb</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1903496994</pqid></control><display><type>article</type><title>On Discriminating between Gamma and Log-logistic Distributions in Case of Progressive Type II Censoring</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Ahmed Elsherpieny, Elsayed ; Zeyada Muhammed, Hiba ; Usama Mohamed Mohamed Radwan, Noha</creator><creatorcontrib>Ahmed Elsherpieny, Elsayed ; Zeyada Muhammed, Hiba ; Usama Mohamed Mohamed Radwan, Noha</creatorcontrib><description>Gamma and log-logistic distributions are two popular distributions for analyzing lifetime data. In this paper, the problem of discriminating between these two distribution functions is considered in case of progressive type II censoring. The ratio of the maximized likelihood test (RML) is used to discriminate between them. Some simulation experiments were performed to see how the probability of correct selection (PCS) under each model work for small sample sizes. Real data life is analyzed to see how the proposed method works in practice. As a special case of progressive type II censoring, the problem of discriminating between gamma and log-logistic in case of complete samples is considered. The RML and the ratio of Minimized Kullback-Leibler Divergence (RMKLD) tests are used to discriminate between them. The asymptotic results are used to estimate the PCS which is used to calculate the minimum sample size required for discriminating between two distributions. Two real life data are analyzed.</description><identifier>ISSN: 1816-2711</identifier><identifier>EISSN: 2220-5810</identifier><identifier>DOI: 10.18187/pjsor.v13i1.1524</identifier><language>eng</language><publisher>Lahore: University of the Punjab, College of Statistical & Actuarial Science</publisher><subject>Computer simulation ; Distribution functions ; Divergence</subject><ispartof>Pakistan journal of statistics and operation research, 2017-01, Vol.13 (1), p.157</ispartof><rights>Copyright University of the Punjab, College of Statistical & Actuarial Science 2017</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c273t-6ea1ab9365a971a01cb90616a893e7dd1f5af9754b8cc8f72b7689ba39d841db3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Ahmed Elsherpieny, Elsayed</creatorcontrib><creatorcontrib>Zeyada Muhammed, Hiba</creatorcontrib><creatorcontrib>Usama Mohamed Mohamed Radwan, Noha</creatorcontrib><title>On Discriminating between Gamma and Log-logistic Distributions in Case of Progressive Type II Censoring</title><title>Pakistan journal of statistics and operation research</title><description>Gamma and log-logistic distributions are two popular distributions for analyzing lifetime data. In this paper, the problem of discriminating between these two distribution functions is considered in case of progressive type II censoring. The ratio of the maximized likelihood test (RML) is used to discriminate between them. Some simulation experiments were performed to see how the probability of correct selection (PCS) under each model work for small sample sizes. Real data life is analyzed to see how the proposed method works in practice. As a special case of progressive type II censoring, the problem of discriminating between gamma and log-logistic in case of complete samples is considered. The RML and the ratio of Minimized Kullback-Leibler Divergence (RMKLD) tests are used to discriminate between them. The asymptotic results are used to estimate the PCS which is used to calculate the minimum sample size required for discriminating between two distributions. Two real life data are analyzed.</description><subject>Computer simulation</subject><subject>Distribution functions</subject><subject>Divergence</subject><issn>1816-2711</issn><issn>2220-5810</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNotkM1KAzEURoMoWLQP4C7geurcZCY_S6laC4W6qOuQzGSGlDYZk2mlb2_aurqbw7kfB6EnKGcgQPCXYZtCnB2BOphBTaobNCGElEUtoLxFkwyxgnCAezRNyZmSMMkzxyeoX3v85lIT3d55PTrfY2PHX2s9Xuj9XmPtW7wKfbELvUuja870GJ05jC74hJ3Hc50sDh3-iqGPNvuPFm9Og8XLJZ5bn5dl6yO66_Qu2en_fUDfH--b-WexWi-W89dV0RBOx4JZDdpIymqdF-oSGiNLBkwLSS1vW-hq3UleV0Y0jeg4MZwJaTSVraigNfQBPV-9Qww_B5tGtQ2H6PNLBbKklWRSVpmCK9XEkFK0nRpyAB1PCkp1SaouSdUlqTonpX96iWxb</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>Ahmed Elsherpieny, Elsayed</creator><creator>Zeyada Muhammed, Hiba</creator><creator>Usama Mohamed Mohamed Radwan, Noha</creator><general>University of the Punjab, College of Statistical & Actuarial Science</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20170101</creationdate><title>On Discriminating between Gamma and Log-logistic Distributions in Case of Progressive Type II Censoring</title><author>Ahmed Elsherpieny, Elsayed ; Zeyada Muhammed, Hiba ; Usama Mohamed Mohamed Radwan, Noha</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c273t-6ea1ab9365a971a01cb90616a893e7dd1f5af9754b8cc8f72b7689ba39d841db3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Computer simulation</topic><topic>Distribution functions</topic><topic>Divergence</topic><toplevel>online_resources</toplevel><creatorcontrib>Ahmed Elsherpieny, Elsayed</creatorcontrib><creatorcontrib>Zeyada Muhammed, Hiba</creatorcontrib><creatorcontrib>Usama Mohamed Mohamed Radwan, Noha</creatorcontrib><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Access via ProQuest (Open Access)</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><jtitle>Pakistan journal of statistics and operation research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ahmed Elsherpieny, Elsayed</au><au>Zeyada Muhammed, Hiba</au><au>Usama Mohamed Mohamed Radwan, Noha</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On Discriminating between Gamma and Log-logistic Distributions in Case of Progressive Type II Censoring</atitle><jtitle>Pakistan journal of statistics and operation research</jtitle><date>2017-01-01</date><risdate>2017</risdate><volume>13</volume><issue>1</issue><spage>157</spage><pages>157-</pages><issn>1816-2711</issn><eissn>2220-5810</eissn><abstract>Gamma and log-logistic distributions are two popular distributions for analyzing lifetime data. In this paper, the problem of discriminating between these two distribution functions is considered in case of progressive type II censoring. The ratio of the maximized likelihood test (RML) is used to discriminate between them. Some simulation experiments were performed to see how the probability of correct selection (PCS) under each model work for small sample sizes. Real data life is analyzed to see how the proposed method works in practice. As a special case of progressive type II censoring, the problem of discriminating between gamma and log-logistic in case of complete samples is considered. The RML and the ratio of Minimized Kullback-Leibler Divergence (RMKLD) tests are used to discriminate between them. The asymptotic results are used to estimate the PCS which is used to calculate the minimum sample size required for discriminating between two distributions. Two real life data are analyzed.</abstract><cop>Lahore</cop><pub>University of the Punjab, College of Statistical & Actuarial Science</pub><doi>10.18187/pjsor.v13i1.1524</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1816-2711 |
ispartof | Pakistan journal of statistics and operation research, 2017-01, Vol.13 (1), p.157 |
issn | 1816-2711 2220-5810 |
language | eng |
recordid | cdi_proquest_journals_1903496994 |
source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
subjects | Computer simulation Distribution functions Divergence |
title | On Discriminating between Gamma and Log-logistic Distributions in Case of Progressive Type II Censoring |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T17%3A35%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=On%20Discriminating%20between%20Gamma%20and%20Log-logistic%20Distributions%20in%20Case%20of%20Progressive%20Type%20II%20Censoring&rft.jtitle=Pakistan%20journal%20of%20statistics%20and%20operation%20research&rft.au=Ahmed%20Elsherpieny,%20Elsayed&rft.date=2017-01-01&rft.volume=13&rft.issue=1&rft.spage=157&rft.pages=157-&rft.issn=1816-2711&rft.eissn=2220-5810&rft_id=info:doi/10.18187/pjsor.v13i1.1524&rft_dat=%3Cproquest_cross%3E1903496994%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1903496994&rft_id=info:pmid/&rfr_iscdi=true |