Modeling count data:
This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modelin...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY, USA
Cambridge University Press
2014
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| Subjects: | |
| Online Access: | BSB01 FHN01 UBG01 Volltext |
| Summary: | This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions. It begins by demonstrating the fundamentals of modeling count data, including a thorough presentation of the Poisson model. It then works up to an analysis of the problem of overdispersion and of the negative binomial model, and finally to the many variations that can be made to the base count models. Examples in Stata, R, and SAS code enable readers to adapt models for their own purposes, making the text an ideal resource for researchers working in health, ecology, econometrics, transportation, and other fields |
| Physical Description: | 1 Online-Ressource (xv, 283 Seiten) |
| ISBN: | 9781139236065 |
| DOI: | 10.1017/CBO9781139236065 |
Staff View
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| 505 | 8 | |a Machine generated contents note: Preface; 1. Varieties of count data; 2. Poisson regression; 3. Testing overdispersion; 4. Assessment of fit; 5. Negative binomial regression; 6. Poisson inverse Gaussian regression; 7. Problems with zeros; 8. Modeling under-dispersed count data - generalized Poisson; 9. Complex data: more advanced models; Appendix A: SAS code; References; Index | |
| 520 | |a This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions. It begins by demonstrating the fundamentals of modeling count data, including a thorough presentation of the Poisson model. It then works up to an analysis of the problem of overdispersion and of the negative binomial model, and finally to the many variations that can be made to the base count models. Examples in Stata, R, and SAS code enable readers to adapt models for their own purposes, making the text an ideal resource for researchers working in health, ecology, econometrics, transportation, and other fields | ||
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| contents | Machine generated contents note: Preface; 1. Varieties of count data; 2. Poisson regression; 3. Testing overdispersion; 4. Assessment of fit; 5. Negative binomial regression; 6. Poisson inverse Gaussian regression; 7. Problems with zeros; 8. Modeling under-dispersed count data - generalized Poisson; 9. Complex data: more advanced models; Appendix A: SAS code; References; Index |
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| dewey-full | 519.5/35 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/35 |
| dewey-search | 519.5/35 |
| dewey-sort | 3519.5 235 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| doi_str_mv | 10.1017/CBO9781139236065 |
| format | Electronic eBook |
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| institution | BVB |
| isbn | 9781139236065 |
| language | English |
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| spelling | Hilbe, Joseph M. 1944-2017 Verfasser (DE-588)128751851 aut Modeling count data Joseph M. Hilbe (Arizona State University and Jet Propulsion Laboratory, California Institute of Technology) New York, NY, USA Cambridge University Press 2014 1 Online-Ressource (xv, 283 Seiten) txt rdacontent c rdamedia cr rdacarrier Machine generated contents note: Preface; 1. Varieties of count data; 2. Poisson regression; 3. Testing overdispersion; 4. Assessment of fit; 5. Negative binomial regression; 6. Poisson inverse Gaussian regression; 7. Problems with zeros; 8. Modeling under-dispersed count data - generalized Poisson; 9. Complex data: more advanced models; Appendix A: SAS code; References; Index This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions. It begins by demonstrating the fundamentals of modeling count data, including a thorough presentation of the Poisson model. It then works up to an analysis of the problem of overdispersion and of the negative binomial model, and finally to the many variations that can be made to the base count models. Examples in Stata, R, and SAS code enable readers to adapt models for their own purposes, making the text an ideal resource for researchers working in health, ecology, econometrics, transportation, and other fields Statistik Multivariate analysis Statistics Linear models (Statistics) Datenanalyse (DE-588)4123037-1 gnd rswk-swf Zähldaten (DE-588)4588029-3 gnd rswk-swf Zähldaten (DE-588)4588029-3 s Datenanalyse (DE-588)4123037-1 s DE-604 Erscheint auch als Druckausgabe, Hardcover 978-1-107-02833-3 Erscheint auch als Druckausgabe, Paperback 978-1-107-61125-2 https://doi.org/10.1017/CBO9781139236065 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Hilbe, Joseph M. 1944-2017 Modeling count data Machine generated contents note: Preface; 1. Varieties of count data; 2. Poisson regression; 3. Testing overdispersion; 4. Assessment of fit; 5. Negative binomial regression; 6. Poisson inverse Gaussian regression; 7. Problems with zeros; 8. Modeling under-dispersed count data - generalized Poisson; 9. Complex data: more advanced models; Appendix A: SAS code; References; Index Statistik Multivariate analysis Statistics Linear models (Statistics) Datenanalyse (DE-588)4123037-1 gnd Zähldaten (DE-588)4588029-3 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4588029-3 |
| title | Modeling count data |
| title_auth | Modeling count data |
| title_exact_search | Modeling count data |
| title_full | Modeling count data Joseph M. Hilbe (Arizona State University and Jet Propulsion Laboratory, California Institute of Technology) |
| title_fullStr | Modeling count data Joseph M. Hilbe (Arizona State University and Jet Propulsion Laboratory, California Institute of Technology) |
| title_full_unstemmed | Modeling count data Joseph M. Hilbe (Arizona State University and Jet Propulsion Laboratory, California Institute of Technology) |
| title_short | Modeling count data |
| title_sort | modeling count data |
| topic | Statistik Multivariate analysis Statistics Linear models (Statistics) Datenanalyse (DE-588)4123037-1 gnd Zähldaten (DE-588)4588029-3 gnd |
| topic_facet | Statistik Multivariate analysis Statistics Linear models (Statistics) Datenanalyse Zähldaten |
| url | https://doi.org/10.1017/CBO9781139236065 |
| work_keys_str_mv | AT hilbejosephm modelingcountdata |