Loading…
Power Lindley distribution and associated inference
A new two-parameter power Lindley distribution is introduced and its properties are discussed. These include the shapes of the density and hazard rate functions, the moments, skewness and kurtosis measures, the quantile function, and the limiting distributions of order statistics. Maximum likelihood...
Saved in:
Published in: | Computational statistics & data analysis 2013-08, Vol.64, p.20-33 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c333t-39d018fd6c489f3fd52e8b7b529e1d0b626cde2f92c0ad15c7c52b57c48142193 |
---|---|
cites | cdi_FETCH-LOGICAL-c333t-39d018fd6c489f3fd52e8b7b529e1d0b626cde2f92c0ad15c7c52b57c48142193 |
container_end_page | 33 |
container_issue | |
container_start_page | 20 |
container_title | Computational statistics & data analysis |
container_volume | 64 |
creator | Ghitany, M.E. Al-Mutairi, D.K. Balakrishnan, N. Al-Enezi, L.J. |
description | A new two-parameter power Lindley distribution is introduced and its properties are discussed. These include the shapes of the density and hazard rate functions, the moments, skewness and kurtosis measures, the quantile function, and the limiting distributions of order statistics. Maximum likelihood estimation of the parameters and their estimated asymptotic standard errors are derived. Three algorithms are proposed for generating random data from the proposed distribution. A simulation study is carried out to examine the bias and mean square error of the maximum likelihood estimators of the parameters as well as the coverage probability and the width of the confidence interval for each parameter. An application of the model to a real data set is presented finally and compared with the fit attained by some other well-known two-parameter distributions. |
doi_str_mv | 10.1016/j.csda.2013.02.026 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1372635389</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0167947313000820</els_id><sourcerecordid>1372635389</sourcerecordid><originalsourceid>FETCH-LOGICAL-c333t-39d018fd6c489f3fd52e8b7b529e1d0b626cde2f92c0ad15c7c52b57c48142193</originalsourceid><addsrcrecordid>eNp9kMFKxDAURYMoOI7-gKsu3bQmL03SghsZdBQGdKHrkCYvkKHTjElHmb-3ZVwLF97mnAf3EnLLaMUok_fbymZnKqCMVxSmyDOyYI2CUnEB52QxQapsa8UvyVXOW0op1KpZEP4efzAVmzC4Ho-FC3lMoTuMIQ6FGVxhco42mBFdEQaPCQeL1-TCmz7jzd9dks_np4_VS7l5W7-uHjel5ZyPJW8dZY130tZN67l3ArDpVCegReZoJ0Fah-BbsNQ4JqyyAjqhJpzVwFq-JHenv_sUvw6YR70L2WLfmwHjIWvGFUgueDOjcEJtijkn9Hqfws6ko2ZUzwvprZ4X0vNCmsIUOUkPJwmnEt8Bk842zAVdSGhH7WL4T_8FX4Vujg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1372635389</pqid></control><display><type>article</type><title>Power Lindley distribution and associated inference</title><source>Elsevier</source><source>Backfile Package - Computer Science (Legacy) [YCS]</source><source>Backfile Package - Mathematics (Legacy) [YMT]</source><source>Backfile Package - Decision Sciences [YDT]</source><creator>Ghitany, M.E. ; Al-Mutairi, D.K. ; Balakrishnan, N. ; Al-Enezi, L.J.</creator><creatorcontrib>Ghitany, M.E. ; Al-Mutairi, D.K. ; Balakrishnan, N. ; Al-Enezi, L.J.</creatorcontrib><description>A new two-parameter power Lindley distribution is introduced and its properties are discussed. These include the shapes of the density and hazard rate functions, the moments, skewness and kurtosis measures, the quantile function, and the limiting distributions of order statistics. Maximum likelihood estimation of the parameters and their estimated asymptotic standard errors are derived. Three algorithms are proposed for generating random data from the proposed distribution. A simulation study is carried out to examine the bias and mean square error of the maximum likelihood estimators of the parameters as well as the coverage probability and the width of the confidence interval for each parameter. An application of the model to a real data set is presented finally and compared with the fit attained by some other well-known two-parameter distributions.</description><identifier>ISSN: 0167-9473</identifier><identifier>EISSN: 1872-7352</identifier><identifier>DOI: 10.1016/j.csda.2013.02.026</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>Algorithms ; Asymptotic properties ; Computer simulation ; Density ; Inference ; Lambert function ; Mathematical analysis ; Mathematical models ; Maximum likelihood estimator ; Power Lindley distribution ; Simulation algorithms ; Statistics</subject><ispartof>Computational statistics & data analysis, 2013-08, Vol.64, p.20-33</ispartof><rights>2013 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c333t-39d018fd6c489f3fd52e8b7b529e1d0b626cde2f92c0ad15c7c52b57c48142193</citedby><cites>FETCH-LOGICAL-c333t-39d018fd6c489f3fd52e8b7b529e1d0b626cde2f92c0ad15c7c52b57c48142193</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0167947313000820$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3427,3438,3562,27923,27924,45971,45990,46002</link.rule.ids></links><search><creatorcontrib>Ghitany, M.E.</creatorcontrib><creatorcontrib>Al-Mutairi, D.K.</creatorcontrib><creatorcontrib>Balakrishnan, N.</creatorcontrib><creatorcontrib>Al-Enezi, L.J.</creatorcontrib><title>Power Lindley distribution and associated inference</title><title>Computational statistics & data analysis</title><description>A new two-parameter power Lindley distribution is introduced and its properties are discussed. These include the shapes of the density and hazard rate functions, the moments, skewness and kurtosis measures, the quantile function, and the limiting distributions of order statistics. Maximum likelihood estimation of the parameters and their estimated asymptotic standard errors are derived. Three algorithms are proposed for generating random data from the proposed distribution. A simulation study is carried out to examine the bias and mean square error of the maximum likelihood estimators of the parameters as well as the coverage probability and the width of the confidence interval for each parameter. An application of the model to a real data set is presented finally and compared with the fit attained by some other well-known two-parameter distributions.</description><subject>Algorithms</subject><subject>Asymptotic properties</subject><subject>Computer simulation</subject><subject>Density</subject><subject>Inference</subject><subject>Lambert function</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Maximum likelihood estimator</subject><subject>Power Lindley distribution</subject><subject>Simulation algorithms</subject><subject>Statistics</subject><issn>0167-9473</issn><issn>1872-7352</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNp9kMFKxDAURYMoOI7-gKsu3bQmL03SghsZdBQGdKHrkCYvkKHTjElHmb-3ZVwLF97mnAf3EnLLaMUok_fbymZnKqCMVxSmyDOyYI2CUnEB52QxQapsa8UvyVXOW0op1KpZEP4efzAVmzC4Ho-FC3lMoTuMIQ6FGVxhco42mBFdEQaPCQeL1-TCmz7jzd9dks_np4_VS7l5W7-uHjel5ZyPJW8dZY130tZN67l3ArDpVCegReZoJ0Fah-BbsNQ4JqyyAjqhJpzVwFq-JHenv_sUvw6YR70L2WLfmwHjIWvGFUgueDOjcEJtijkn9Hqfws6ko2ZUzwvprZ4X0vNCmsIUOUkPJwmnEt8Bk842zAVdSGhH7WL4T_8FX4Vujg</recordid><startdate>20130801</startdate><enddate>20130801</enddate><creator>Ghitany, M.E.</creator><creator>Al-Mutairi, D.K.</creator><creator>Balakrishnan, N.</creator><creator>Al-Enezi, L.J.</creator><general>Elsevier B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20130801</creationdate><title>Power Lindley distribution and associated inference</title><author>Ghitany, M.E. ; Al-Mutairi, D.K. ; Balakrishnan, N. ; Al-Enezi, L.J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c333t-39d018fd6c489f3fd52e8b7b529e1d0b626cde2f92c0ad15c7c52b57c48142193</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Algorithms</topic><topic>Asymptotic properties</topic><topic>Computer simulation</topic><topic>Density</topic><topic>Inference</topic><topic>Lambert function</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Maximum likelihood estimator</topic><topic>Power Lindley distribution</topic><topic>Simulation algorithms</topic><topic>Statistics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ghitany, M.E.</creatorcontrib><creatorcontrib>Al-Mutairi, D.K.</creatorcontrib><creatorcontrib>Balakrishnan, N.</creatorcontrib><creatorcontrib>Al-Enezi, L.J.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computational statistics & data analysis</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ghitany, M.E.</au><au>Al-Mutairi, D.K.</au><au>Balakrishnan, N.</au><au>Al-Enezi, L.J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Power Lindley distribution and associated inference</atitle><jtitle>Computational statistics & data analysis</jtitle><date>2013-08-01</date><risdate>2013</risdate><volume>64</volume><spage>20</spage><epage>33</epage><pages>20-33</pages><issn>0167-9473</issn><eissn>1872-7352</eissn><abstract>A new two-parameter power Lindley distribution is introduced and its properties are discussed. These include the shapes of the density and hazard rate functions, the moments, skewness and kurtosis measures, the quantile function, and the limiting distributions of order statistics. Maximum likelihood estimation of the parameters and their estimated asymptotic standard errors are derived. Three algorithms are proposed for generating random data from the proposed distribution. A simulation study is carried out to examine the bias and mean square error of the maximum likelihood estimators of the parameters as well as the coverage probability and the width of the confidence interval for each parameter. An application of the model to a real data set is presented finally and compared with the fit attained by some other well-known two-parameter distributions.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.csda.2013.02.026</doi><tpages>14</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0167-9473 |
ispartof | Computational statistics & data analysis, 2013-08, Vol.64, p.20-33 |
issn | 0167-9473 1872-7352 |
language | eng |
recordid | cdi_proquest_miscellaneous_1372635389 |
source | Elsevier; Backfile Package - Computer Science (Legacy) [YCS]; Backfile Package - Mathematics (Legacy) [YMT]; Backfile Package - Decision Sciences [YDT] |
subjects | Algorithms Asymptotic properties Computer simulation Density Inference Lambert function Mathematical analysis Mathematical models Maximum likelihood estimator Power Lindley distribution Simulation algorithms Statistics |
title | Power Lindley distribution and associated inference |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T09%3A28%3A21IST&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=Power%20Lindley%20distribution%20and%20associated%20inference&rft.jtitle=Computational%20statistics%20&%20data%20analysis&rft.au=Ghitany,%20M.E.&rft.date=2013-08-01&rft.volume=64&rft.spage=20&rft.epage=33&rft.pages=20-33&rft.issn=0167-9473&rft.eissn=1872-7352&rft_id=info:doi/10.1016/j.csda.2013.02.026&rft_dat=%3Cproquest_cross%3E1372635389%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c333t-39d018fd6c489f3fd52e8b7b529e1d0b626cde2f92c0ad15c7c52b57c48142193%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1372635389&rft_id=info:pmid/&rfr_iscdi=true |