| 来源类型 | Article |
| 规范类型 | 其他 |
| DOI | 10.1371/journal.pone.0170920 |
| Fitting power-laws in empirical data with estimators that work for all exponents. | |
| Altmann EG; Hanel R; Corominas-Murtra B; Liu B; Thurner S | |
| 发表日期 | 2017 |
| 出处 | PLOS ONE 12 (2): e0170920 |
| 出版年 | 2017 |
| 语种 | 英语 |
| 摘要 | Most standard methods based on maximum likelihood (ML) estimates of power-law exponents can only be reliably used to identify exponents smaller than minus one. The argument that power laws are otherwise not normalizable, depends on the underlying sample space the data is drawn from, and is true only for sample spaces that are unbounded from above. Power-laws obtained from bounded sample spaces (as is the case for practically all data related problems) are always free of such limitations and maximum likelihood estimates can be obtained for arbitrary powers without restrictions. Here we first derive the appropriate ML estimator for arbitrary exponents of power-law distributions on bounded discrete sample spaces. We then show that an almost identical estimator also works perfectly for continuous data. We implemented this ML estimator and discuss its performance with previous attempts. We present a general recipe of how to use these estimators and present the associated computer codes. |
| 主题 | Advanced Systems Analysis (ASA) |
| URL | http://pure.iiasa.ac.at/id/eprint/14397/ |
| 来源智库 | International Institute for Applied Systems Analysis (Austria) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/130941 |
| 推荐引用方式 GB/T 7714 | Altmann EG,Hanel R,Corominas-Murtra B,et al. Fitting power-laws in empirical data with estimators that work for all exponents.. 2017. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| journal.pone.0170920（2163KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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