A Poisson common factor model for projecting mortality and life expectancy jointly for females and males
We examine the application of a Poisson common factor model for the projection of mortality jointly for females and males. The model structure is an extension of the classical Lee-Carter method in which there is a common factor for the aggregate population, while a number of additional sex-specific...
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| Veröffentlicht in: | Population studies 2013-03, Vol.67 (1), p.111-126 |
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| creator | Li, Jackie |
| description | We examine the application of a Poisson common factor model for the projection of mortality jointly for females and males. The model structure is an extension of the classical Lee-Carter method in which there is a common factor for the aggregate population, while a number of additional sex-specific factors can also be incorporated. The Poisson distribution is a natural choice for modelling the number of deaths, and its use provides a formal statistical framework for model selection, parameter estimation, and data analysis. Our results for Australian data show that this model leads to projected life expectancy values similar to those produced by the separate projection of mortality for females and males, but possesses the additional advantage of ensuring that the projected male-to-female ratio for death rates at each age converges to a constant. Moreover, the randomness of the corresponding residuals indicates that the model fit is satisfactory. |
| doi_str_mv | 10.1080/00324728.2012.689316 |
| format | Article |
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The model structure is an extension of the classical Lee-Carter method in which there is a common factor for the aggregate population, while a number of additional sex-specific factors can also be incorporated. The Poisson distribution is a natural choice for modelling the number of deaths, and its use provides a formal statistical framework for model selection, parameter estimation, and data analysis. Our results for Australian data show that this model leads to projected life expectancy values similar to those produced by the separate projection of mortality for females and males, but possesses the additional advantage of ensuring that the projected male-to-female ratio for death rates at each age converges to a constant. Moreover, the randomness of the corresponding residuals indicates that the model fit is satisfactory.</description><identifier>ISSN: 0032-4728</identifier><identifier>EISSN: 1477-4747</identifier><identifier>DOI: 10.1080/00324728.2012.689316</identifier><identifier>PMID: 22788919</identifier><identifier>CODEN: POSTA4</identifier><language>eng</language><publisher>England: Taylor & Francis Group</publisher><subject>Adolescent ; Adult ; Age ; Age groups ; Aged ; Aged, 80 and over ; Australia ; Australia - epidemiology ; Australian mortality ; Child ; Child, Preschool ; Choices ; Data analysis ; Death ; Death rate ; death rates of both sexes ; Demography ; Economic models ; Female ; Females ; Forecasting models ; Gender ; Humans ; Infant ; Infant, Newborn ; Life Expectancy ; Life span ; Longevity ; Male ; Males ; Middle Aged ; Modeling ; Models, Statistical ; Mortality ; Mortality Rates ; Parameter estimation ; Parametric models ; Poisson common factor model ; Poisson Distribution ; Population studies ; Randomness ; Reproduction ; Statistical methods ; Statistics ; Time series models ; Values ; Young Adult</subject><ispartof>Population studies, 2013-03, Vol.67 (1), p.111-126</ispartof><rights>Copyright Population Investigation Committee 2013</rights><rights>Copyright © 2013 Population Investigation Committee</rights><rights>Copyright Population Investigation Committee, London School of Economics and Political Science 2013</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c545t-421c7911e4e6e23d20022401a75a70f22438a8a993ef861214fc04bb0bda283c3</citedby><cites>FETCH-LOGICAL-c545t-421c7911e4e6e23d20022401a75a70f22438a8a993ef861214fc04bb0bda283c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/43287867$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/43287867$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,27903,27904,33753,33754,57995,58228</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22788919$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Jackie</creatorcontrib><title>A Poisson common factor model for projecting mortality and life expectancy jointly for females and males</title><title>Population studies</title><addtitle>Popul Stud (Camb)</addtitle><description>We examine the application of a Poisson common factor model for the projection of mortality jointly for females and males. The model structure is an extension of the classical Lee-Carter method in which there is a common factor for the aggregate population, while a number of additional sex-specific factors can also be incorporated. The Poisson distribution is a natural choice for modelling the number of deaths, and its use provides a formal statistical framework for model selection, parameter estimation, and data analysis. Our results for Australian data show that this model leads to projected life expectancy values similar to those produced by the separate projection of mortality for females and males, but possesses the additional advantage of ensuring that the projected male-to-female ratio for death rates at each age converges to a constant. Moreover, the randomness of the corresponding residuals indicates that the model fit is satisfactory.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Age</subject><subject>Age groups</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Australia</subject><subject>Australia - epidemiology</subject><subject>Australian mortality</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>Choices</subject><subject>Data analysis</subject><subject>Death</subject><subject>Death rate</subject><subject>death rates of both sexes</subject><subject>Demography</subject><subject>Economic models</subject><subject>Female</subject><subject>Females</subject><subject>Forecasting models</subject><subject>Gender</subject><subject>Humans</subject><subject>Infant</subject><subject>Infant, Newborn</subject><subject>Life Expectancy</subject><subject>Life span</subject><subject>Longevity</subject><subject>Male</subject><subject>Males</subject><subject>Middle Aged</subject><subject>Modeling</subject><subject>Models, Statistical</subject><subject>Mortality</subject><subject>Mortality Rates</subject><subject>Parameter estimation</subject><subject>Parametric models</subject><subject>Poisson common factor model</subject><subject>Poisson Distribution</subject><subject>Population studies</subject><subject>Randomness</subject><subject>Reproduction</subject><subject>Statistical methods</subject><subject>Statistics</subject><subject>Time series models</subject><subject>Values</subject><subject>Young Adult</subject><issn>0032-4728</issn><issn>1477-4747</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BHHNA</sourceid><recordid>eNqNkk1v1DAQhi0EotvCPwAUiQuXLOOPxPYJVRUUpEpwgLPldWxI5MSLnRXk3zO7aXvgAD3NyO8z49G8Q8gLClsKCt4CcCYkU1sGlG1bpTltH5ENFVLWQgr5mGyOSH1kzsh5KQMA6EbKp-SMMamUpnpDflxWX1JfSpoql8YRQ7BuTrkaU-djFTDb5zR4N_fTd3zMs439vFR26qrYB1_533sU7eSWakj9NMflVBT8aKMvJ-6UPSNPgo3FP7-NF-Tbh_dfrz7WN5-vP11d3tSuEc1cC0ad1JR64VvPeMcAGBNArWyshIA5V1ZZrbkPqqWMiuBA7Haw6yxT3PEL8mbti2P_PPgym7EvzsdoJ58OxVDeCgmMNvQBKOMKGi0eguLe0Ye2_T_KlMS2SjeIvv4LHdIhT7ieE6WbljFASqyUy6mU7IPZ5360eTEUzPEUzN0pmOMpmPUUsOzVbfPDbvTdfdGd9wi8XIGhoOH3uuD4tWol6u9WvZ_Q0dH-Sjl2ZrZLTDlkdLwvhv9zhD8SxMl0</recordid><startdate>20130301</startdate><enddate>20130301</enddate><creator>Li, Jackie</creator><general>Taylor & Francis Group</general><general>Taylor & Francis</general><general>Population Investigation Committee, London School of Economics and Political Science</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>7U4</scope><scope>8BJ</scope><scope>BHHNA</scope><scope>DWI</scope><scope>FQK</scope><scope>JBE</scope><scope>WZK</scope><scope>7X8</scope><scope>7ST</scope><scope>7U6</scope><scope>C1K</scope><scope>SOI</scope></search><sort><creationdate>20130301</creationdate><title>A Poisson common factor model for projecting mortality and life expectancy jointly for females and males</title><author>Li, Jackie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c545t-421c7911e4e6e23d20022401a75a70f22438a8a993ef861214fc04bb0bda283c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Age</topic><topic>Age groups</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Australia</topic><topic>Australia - epidemiology</topic><topic>Australian mortality</topic><topic>Child</topic><topic>Child, Preschool</topic><topic>Choices</topic><topic>Data analysis</topic><topic>Death</topic><topic>Death rate</topic><topic>death rates of both sexes</topic><topic>Demography</topic><topic>Economic models</topic><topic>Female</topic><topic>Females</topic><topic>Forecasting models</topic><topic>Gender</topic><topic>Humans</topic><topic>Infant</topic><topic>Infant, Newborn</topic><topic>Life Expectancy</topic><topic>Life span</topic><topic>Longevity</topic><topic>Male</topic><topic>Males</topic><topic>Middle Aged</topic><topic>Modeling</topic><topic>Models, Statistical</topic><topic>Mortality</topic><topic>Mortality Rates</topic><topic>Parameter estimation</topic><topic>Parametric models</topic><topic>Poisson common factor model</topic><topic>Poisson Distribution</topic><topic>Population studies</topic><topic>Randomness</topic><topic>Reproduction</topic><topic>Statistical methods</topic><topic>Statistics</topic><topic>Time series models</topic><topic>Values</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Jackie</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Sociological Abstracts (pre-2017)</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Sociological Abstracts</collection><collection>Sociological Abstracts</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>Sociological Abstracts (Ovid)</collection><collection>MEDLINE - Academic</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><jtitle>Population studies</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Jackie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Poisson common factor model for projecting mortality and life expectancy jointly for females and males</atitle><jtitle>Population studies</jtitle><addtitle>Popul Stud (Camb)</addtitle><date>2013-03-01</date><risdate>2013</risdate><volume>67</volume><issue>1</issue><spage>111</spage><epage>126</epage><pages>111-126</pages><issn>0032-4728</issn><eissn>1477-4747</eissn><coden>POSTA4</coden><abstract>We examine the application of a Poisson common factor model for the projection of mortality jointly for females and males. The model structure is an extension of the classical Lee-Carter method in which there is a common factor for the aggregate population, while a number of additional sex-specific factors can also be incorporated. The Poisson distribution is a natural choice for modelling the number of deaths, and its use provides a formal statistical framework for model selection, parameter estimation, and data analysis. Our results for Australian data show that this model leads to projected life expectancy values similar to those produced by the separate projection of mortality for females and males, but possesses the additional advantage of ensuring that the projected male-to-female ratio for death rates at each age converges to a constant. Moreover, the randomness of the corresponding residuals indicates that the model fit is satisfactory.</abstract><cop>England</cop><pub>Taylor & Francis Group</pub><pmid>22788919</pmid><doi>10.1080/00324728.2012.689316</doi><tpages>16</tpages></addata></record> |
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| language | eng |
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| source | Jstor Complete Legacy; MEDLINE; Sociological Abstracts |
| subjects | Adolescent Adult Age Age groups Aged Aged, 80 and over Australia Australia - epidemiology Australian mortality Child Child, Preschool Choices Data analysis Death Death rate death rates of both sexes Demography Economic models Female Females Forecasting models Gender Humans Infant Infant, Newborn Life Expectancy Life span Longevity Male Males Middle Aged Modeling Models, Statistical Mortality Mortality Rates Parameter estimation Parametric models Poisson common factor model Poisson Distribution Population studies Randomness Reproduction Statistical methods Statistics Time series models Values Young Adult |
| title | A Poisson common factor model for projecting mortality and life expectancy jointly for females and males |
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