Bias in Zipf’s law estimators
Abstract The prevailing maximum likelihood estimators for inferring power law models from rank-frequency data are biased. The source of this bias is an inappropriate likelihood function. The correct likelihood function is derived and shown to be computationally intractable. A more computationally ef...
Guardado en:
Autores principales: | Charlie Pilgrim, Thomas T Hills |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/9b29ac7b9d07447eaecece75d6538d88 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
The statistics of urban scaling and their connection to Zipf's law.
por: Andres Gomez-Lievano, et al.
Publicado: (2012) -
Zipf's Law for cities: estimation of regression function parameters based on the weight of American urban areas and Polish towns
por: Sokołowski Dariusz, et al.
Publicado: (2021) -
Systematic biases in human heading estimation.
por: Luigi F Cuturi, et al.
Publicado: (2013) -
Exposure misclassification bias in the estimation of vaccine effectiveness.
por: Ulrike Baum, et al.
Publicado: (2021) -
An empirical investigation of deviations from the Beer–Lambert law in optical estimation of lactate
por: M. Mamouei, et al.
Publicado: (2021)