Bayesian estimation and regularization techniques in categorical data analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F25%3A00643607" target="_blank" >RIV/67985807:_____/25:00643607 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11320/25:10507346
Result on the web
<a href="https://doi.org/10.2478/jamsi-2025-0011" target="_blank" >https://doi.org/10.2478/jamsi-2025-0011</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.2478/jamsi-2025-0011" target="_blank" >10.2478/jamsi-2025-0011</a>
Alternative languages
Result language
angličtina
Original language name
Bayesian estimation and regularization techniques in categorical data analysis
Original language description
This paper explores Bayesian estimation for categorical data, focusing on simple yet effective models that provide a foundation for applying more advanced methods accurately and reliably in real-world applications. We begin by revisiting Bayesian estimators for the binomial distribution and investigating their properties. Next, we develop hypothesis tests for categorical data (sign test, homogeneity test, symmetry test) based on regularized maximum likelihood estimates of the probabilities. Finally, we formulate regularized versions of common association measures for contingency tables and study the regularized version of mutual information, particular for the situation where the regularized version can effectively handle zero counts.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/GA24-11146S" target="_blank" >GA24-11146S: Maximal Entropy Portfolio</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of applied mathematics, statistics and informatics
ISSN
1336-9180
e-ISSN
1339-0015
Volume of the periodical
21
Issue of the periodical within the volume
2
Country of publishing house
SK - SLOVAKIA
Number of pages
18
Pages from-to
105-122
UT code for WoS article
001649863600004
EID of the result in the Scopus database
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