On the Bayesian Interpretation of Penalized Statistical Estimators
Result description
The aim of this work is to search for intuitive interpretations of penalized statistical estimators. Penalized estimates of the parameters of three models obtained by Bayesian reasoning are explained here to correspond to the intuition. First, the paper considers Bayesian estimates of the mean and covariance matrix for the multivariate normal distribution. Second, a connection of a robust regularized version of Mahalanobis distance with Bayesian estimation is discussed. Third, regularization networks, which represent a common nonparametric tool for regression modeling, are presented as Bayesian methods as well. On the whole, selected important multivariate and/or regression models are considered and novel interpretations are formulated.
Keywords
Bayesian estimationregularizationpenalizationrobustnessregression
The result's identifiers
Result code in IS VaVaI
Alternative codes found
RIV/67985807:_____/23:00579680
Result on the web
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
On the Bayesian Interpretation of Penalized Statistical Estimators
Original language description
The aim of this work is to search for intuitive interpretations of penalized statistical estimators. Penalized estimates of the parameters of three models obtained by Bayesian reasoning are explained here to correspond to the intuition. First, the paper considers Bayesian estimates of the mean and covariance matrix for the multivariate normal distribution. Second, a connection of a robust regularized version of Mahalanobis distance with Bayesian estimation is discussed. Third, regularization networks, which represent a common nonparametric tool for regression modeling, are presented as Bayesian methods as well. On the whole, selected important multivariate and/or regression models are considered and novel interpretations are formulated.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Intelligence and Soft Computing. 22nd International Conference, ICAISC 2023, Proceedings, Part 2
ISBN
978-3-031-42507-3
ISSN
—
e-ISSN
—
Number of pages
10
Pages from-to
343-352
Publisher name
Springer
Place of publication
Cham
Event location
Zakopane
Event date
Jul 18, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001155257400031
Basic information
Result type
D - Article in proceedings
OECD FORD
Statistics and probability
Year of implementation
2023