A numerical method to compute Fisher information for a special case of heterogeneous negative binomial regression
| Autor: | Kenneth C. Land, Xin Guo, Yue Wang, Qiang Fu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Applied Mathematics
Numerical analysis 010102 general mathematics Negative binomial distribution Parameterized complexity Regression analysis General Medicine 01 natural sciences 010101 applied mathematics symbols.namesake Overdispersion Statistics Range (statistics) symbols Statistics::Methodology 0101 mathematics Special case Fisher information Analysis Mathematics |
| Zdroj: | Communications on Pure & Applied Analysis. 19:4179-4189 |
| ISSN: | 1553-5258 |
| Popis: | Negative binomial regression has been widely applied in various research settings to account for counts with overdispersion. Yet, when the gamma scale parameter, \begin{document}$ \nu $\end{document} , is parameterized, there is no direct algorithmic solution to the Fisher Information matrix of the associated heterogeneous negative binomial regression, which seriously limits its applications to a wide range of complex problems. In this research, we propose a numerical method to calculate the Fisher information of heterogeneous negative binomial regression and accordingly develop a preliminary framework for analyzing incomplete counts with overdispersion. This method is implemented in R and illustrated using an empirical example of teenage drug use in America. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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