An empirical approach to determine a threshold for assessing overdispersion in Poisson and negative binomial models for count data
Overdispersion is a problem encountered in the analysis of count data that can lead to invalid inference if unaddressed. Decision about whether data are overdispersed is often reached by checking whether the ratio of the Pearson chi-square statistic to its degrees of freedom is greater than one; how...
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| Published in | Communications in statistics. Simulation and computation Vol. 47; no. 6; pp. 1722 - 1738 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Taylor & Francis
05.07.2018
Taylor & Francis Ltd |
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| Online Access | Get full text |
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| Abstract | Overdispersion is a problem encountered in the analysis of count data that can lead to invalid inference if unaddressed. Decision about whether data are overdispersed is often reached by checking whether the ratio of the Pearson chi-square statistic to its degrees of freedom is greater than one; however, there is currently no fixed threshold for declaring the need for statistical intervention. We consider simulated cross-sectional and longitudinal datasets containing varying magnitudes of overdispersion caused by outliers or zero inflation, as well as real datasets, to determine an appropriate threshold value of this statistic which indicates when overdispersion should be addressed. |
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| AbstractList | Overdispersion is a problem encountered in the analysis of count data that can lead to invalid inference if unaddressed. Decision about whether data are overdispersed is often reached by checking whether the ratio of the Pearson chi-square statistic to its degrees of freedom is greater than one; however, there is currently no fixed threshold for declaring the need for statistical intervention. We consider simulated cross-sectional and longitudinal datasets containing varying magnitudes of overdispersion caused by outliers or zero inflation, as well as real datasets, to determine an appropriate threshold value of this statistic which indicates when overdispersion should be addressed. Overdispersion is a problem encountered in the analysis of count data that can lead to invalid inference if unaddressed. Decision about whether data are overdispersed is often reached by checking whether the ratio of the Pearson chi-square statistic to its degrees of freedom is greater than one; however, there is currently no fixed threshold for declaring the need for statistical intervention. We consider simulated cross-sectional and longitudinal datasets containing varying magnitudes of overdispersion caused by outliers or zero inflation, as well as real datasets, to determine an appropriate threshold value of this statistic which indicates when overdispersion should be addressed.Overdispersion is a problem encountered in the analysis of count data that can lead to invalid inference if unaddressed. Decision about whether data are overdispersed is often reached by checking whether the ratio of the Pearson chi-square statistic to its degrees of freedom is greater than one; however, there is currently no fixed threshold for declaring the need for statistical intervention. We consider simulated cross-sectional and longitudinal datasets containing varying magnitudes of overdispersion caused by outliers or zero inflation, as well as real datasets, to determine an appropriate threshold value of this statistic which indicates when overdispersion should be addressed. |
| Author | Egede, Leonard E. Ramakrishnan, Viswanathan Gebregziabher, Mulugeta Hardin, James W. Payne, Elizabeth H. |
| AuthorAffiliation | d Division of Biostatistics, Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA b Health Equity and Rural Outreach Innovation Center (HEROIC), Ralph H. Johnson Department of Veterans Affairs Medical Center, Charleston, SC, USA a Department of Public Health Sciences—Biostatistics, Medical University of South Carolina, Charleston, SC, USA c The EMMES Corporation, Rockville, MD, USA |
| AuthorAffiliation_xml | – name: b Health Equity and Rural Outreach Innovation Center (HEROIC), Ralph H. Johnson Department of Veterans Affairs Medical Center, Charleston, SC, USA – name: d Division of Biostatistics, Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA – name: a Department of Public Health Sciences—Biostatistics, Medical University of South Carolina, Charleston, SC, USA – name: c The EMMES Corporation, Rockville, MD, USA |
| Author_xml | – sequence: 1 givenname: Elizabeth H. surname: Payne fullname: Payne, Elizabeth H. email: epayne@emmes.com organization: The EMMES Corporation – sequence: 2 givenname: Mulugeta surname: Gebregziabher fullname: Gebregziabher, Mulugeta organization: Health Equity and Rural Outreach Innovation Center (HEROIC), Ralph H. Johnson Department of Veterans Affairs Medical Center – sequence: 3 givenname: James W. surname: Hardin fullname: Hardin, James W. organization: Division of Biostatistics, Department of Epidemiology and Biostatistics, University of South Carolina – sequence: 4 givenname: Viswanathan surname: Ramakrishnan fullname: Ramakrishnan, Viswanathan organization: Department of Public Health Sciences-Biostatistics, Medical University of South Carolina – sequence: 5 givenname: Leonard E. surname: Egede fullname: Egede, Leonard E. organization: Health Equity and Rural Outreach Innovation Center (HEROIC), Ralph H. Johnson Department of Veterans Affairs Medical Center |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30555205$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | 62Fxx Chi-square test Computer simulation Count data Datasets Economic models Empirical analysis outliers Outliers (statistics) overdispersion Pearson chi-square Statistical analysis Statistical tests zero inflation |
| Title | An empirical approach to determine a threshold for assessing overdispersion in Poisson and negative binomial models for count data |
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