Observation‐driven models for Poisson counts
This paper is concerned with a general class of observation‐driven models for time series of counts whose conditional distributions given past observations and explanatory variables follow a Poisson distribution. These models provide a flexible framework for modelling a wide range of dependence stru...
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| Veröffentlicht in: | Biometrika 2003-12, Vol.90 (4), p.777-790 |
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| container_end_page | 790 |
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| container_title | Biometrika |
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| creator | Davis, Richard A. Dunsmuir, William T. M. Streett, Sarah B. |
| description | This paper is concerned with a general class of observation‐driven models for time series of counts whose conditional distributions given past observations and explanatory variables follow a Poisson distribution. These models provide a flexible framework for modelling a wide range of dependence structures. Conditions for stationarity and ergodicity of these processes are established from which the large‐sample properties of the maximum likelihood estimators can be derived. Simulations are provided to give additional insight into the finite‐sample behaviour of the estimators. Finally an application to a regression model for daily counts of asthma presentations at a Sydney hospital is described. |
| doi_str_mv | 10.1093/biomet/90.4.777 |
| format | Article |
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M.</creatorcontrib><creatorcontrib>Streett, Sarah B.</creatorcontrib><title>Observation‐driven models for Poisson counts</title><title>Biometrika</title><addtitle>Biometrika</addtitle><description>This paper is concerned with a general class of observation‐driven models for time series of counts whose conditional distributions given past observations and explanatory variables follow a Poisson distribution. These models provide a flexible framework for modelling a wide range of dependence structures. Conditions for stationarity and ergodicity of these processes are established from which the large‐sample properties of the maximum likelihood estimators can be derived. Simulations are provided to give additional insight into the finite‐sample behaviour of the estimators. Finally an application to a regression model for daily counts of asthma presentations at a Sydney hospital is described.</description><subject>Asthma</subject><subject>Asthma hospitalisation</subject><subject>Autoregressive moving average</subject><subject>Ergodic theory</subject><subject>Exact sciences and technology</subject><subject>Inference from stochastic processes; time series analysis</subject><subject>Linear inference, regression</subject><subject>Mathematics</subject><subject>Maximum likelihood estimation</subject><subject>Maximum likelihood estimators</subject><subject>Modeling</subject><subject>Observation‐driven model</subject><subject>Parametric models</subject><subject>Poisson‐valued time series</subject><subject>Probability and statistics</subject><subject>Random variables</subject><subject>Regression analysis</subject><subject>Sciences and techniques of general use</subject><subject>Statistics</subject><subject>Time series</subject><subject>Time series models</subject><issn>0006-3444</issn><issn>1464-3510</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNpFkM1KAzEUhYMoWH_WroQiuJz2Zm6SmVmKWKuIulAUNyHJZHBqOxmTadGdj-Az-iRGptTF4XI53_3hEHJEYUShwLGu3cJ24wJGbJRl2RYZUCZYgpzCNhkAgEiQMbZL9kKY_bWCiwEZ3elg_Up1tWt-vr5LX69sM1y40s7DsHJ-eO_qEFwzNG7ZdOGA7FRqHuzhuu6Tx8nFw_k0ubm7vDo_u0lMvNklRoPRjOZUlIKmpapSzcsMhTUq1ZDbSusCAVWpEYXSojCoM2FYWYkcecpxn5z0e1vv3pc2dHLmlr6JJ2UKVBRcIEZo3EPGuxC8rWTr64Xyn5KC_MtE9pnIAiSTMZM4cd1PeNtas8Hdsl2TK4kq0qg-o1IAjKWOYlFtVFwiswi8dou47HT9owpGzSuvGlOH_x84Ul7kELnjnpuFzvmNjwAshTyPftL7dejsx8ZX_k2KDDMup88v8unplsN0IiTDX-IqlbA</recordid><startdate>20031201</startdate><enddate>20031201</enddate><creator>Davis, Richard A.</creator><creator>Dunsmuir, William T. 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M. ; Streett, Sarah B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c464t-cb0cb41816d612daf2b5d736eca2b08efbb9303adb336ab69c3b76c4df6835253</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Asthma</topic><topic>Asthma hospitalisation</topic><topic>Autoregressive moving average</topic><topic>Ergodic theory</topic><topic>Exact sciences and technology</topic><topic>Inference from stochastic processes; time series analysis</topic><topic>Linear inference, regression</topic><topic>Mathematics</topic><topic>Maximum likelihood estimation</topic><topic>Maximum likelihood estimators</topic><topic>Modeling</topic><topic>Observation‐driven model</topic><topic>Parametric models</topic><topic>Poisson‐valued time series</topic><topic>Probability and statistics</topic><topic>Random variables</topic><topic>Regression analysis</topic><topic>Sciences and techniques of general use</topic><topic>Statistics</topic><topic>Time series</topic><topic>Time series models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Davis, Richard A.</creatorcontrib><creatorcontrib>Dunsmuir, William T. M.</creatorcontrib><creatorcontrib>Streett, Sarah B.</creatorcontrib><collection>Istex</collection><collection>Pascal-Francis</collection><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Biometrika</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Davis, Richard A.</au><au>Dunsmuir, William T. M.</au><au>Streett, Sarah B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Observation‐driven models for Poisson counts</atitle><jtitle>Biometrika</jtitle><addtitle>Biometrika</addtitle><date>2003-12-01</date><risdate>2003</risdate><volume>90</volume><issue>4</issue><spage>777</spage><epage>790</epage><pages>777-790</pages><issn>0006-3444</issn><eissn>1464-3510</eissn><coden>BIOKAX</coden><abstract>This paper is concerned with a general class of observation‐driven models for time series of counts whose conditional distributions given past observations and explanatory variables follow a Poisson distribution. These models provide a flexible framework for modelling a wide range of dependence structures. Conditions for stationarity and ergodicity of these processes are established from which the large‐sample properties of the maximum likelihood estimators can be derived. Simulations are provided to give additional insight into the finite‐sample behaviour of the estimators. Finally an application to a regression model for daily counts of asthma presentations at a Sydney hospital is described.</abstract><cop>Oxford</cop><pub>Oxford University Press</pub><doi>10.1093/biomet/90.4.777</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | Biometrika, 2003-12, Vol.90 (4), p.777-790 |
| issn | 0006-3444 1464-3510 |
| language | eng |
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| source | RePEc; JSTOR Mathematics & Statistics; JSTOR Archive Collection A-Z Listing; Oxford University Press Journals All Titles (1996-Current) |
| subjects | Asthma Asthma hospitalisation Autoregressive moving average Ergodic theory Exact sciences and technology Inference from stochastic processes time series analysis Linear inference, regression Mathematics Maximum likelihood estimation Maximum likelihood estimators Modeling Observation‐driven model Parametric models Poisson‐valued time series Probability and statistics Random variables Regression analysis Sciences and techniques of general use Statistics Time series Time series models |
| title | Observation‐driven models for Poisson counts |
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