Open Access
,
pages 1-8
Bayesian analysis for the Lomax model using noninformative priors
Publication type: Journal Article
Publication date: 2022-10-14
scimago Q3
wos Q2
SJR: 0.319
CiteScore: 1.2
Impact factor: 1.3
ISSN: 24754269, 24754277
Statistics and Probability
Computational Theory and Mathematics
Applied Mathematics
Statistics, Probability and Uncertainty
Analysis
Found
Nothing found, try to update filter.
Found
Nothing found, try to update filter.
Top-30
Journals
|
1
|
|
|
Computation
1 publication, 25%
|
|
|
Journal of Statistical Computation and Simulation
1 publication, 25%
|
|
|
Axioms
1 publication, 25%
|
|
|
Scientific Reports
1 publication, 25%
|
|
|
1
|
Publishers
|
1
2
|
|
|
MDPI
2 publications, 50%
|
|
|
Taylor & Francis
1 publication, 25%
|
|
|
Springer Nature
1 publication, 25%
|
|
|
1
2
|
- We do not take into account publications without a DOI.
- Statistics recalculated weekly.
Are you a researcher?
Create a profile to get free access to personal recommendations for colleagues and new articles.
Metrics
4
Total citations:
4
Citations from 2024:
2
(50%)
Cite this
GOST |
RIS |
BibTex
Cite this
GOST
Copy
He D., Sun D., Zhu Q. Bayesian analysis for the Lomax model using noninformative priors // Statistical Theory and Related Fields. 2022. pp. 1-8.
GOST all authors (up to 50)
Copy
He D., Sun D., Zhu Q. Bayesian analysis for the Lomax model using noninformative priors // Statistical Theory and Related Fields. 2022. pp. 1-8.
Cite this
RIS
Copy
TY - JOUR
DO - 10.1080/24754269.2022.2133466
UR - https://doi.org/10.1080/24754269.2022.2133466
TI - Bayesian analysis for the Lomax model using noninformative priors
T2 - Statistical Theory and Related Fields
AU - He, Daojiang
AU - Sun, Dongchu
AU - Zhu, Qing
PY - 2022
DA - 2022/10/14
PB - Taylor & Francis
SP - 1-8
SN - 2475-4269
SN - 2475-4277
ER -
Cite this
BibTex (up to 50 authors)
Copy
@article{2022_He,
author = {Daojiang He and Dongchu Sun and Qing Zhu},
title = {Bayesian analysis for the Lomax model using noninformative priors},
journal = {Statistical Theory and Related Fields},
year = {2022},
publisher = {Taylor & Francis},
month = {oct},
url = {https://doi.org/10.1080/24754269.2022.2133466},
pages = {1--8},
doi = {10.1080/24754269.2022.2133466}
}