Multilevel maximum likelihood estimation with application to covariance matrices
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F19%3A00486424" target="_blank" >RIV/67985807:_____/19:00486424 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11320/19:10384004
Result on the web
<a href="http://dx.doi.org/10.1080/03610926.2017.1422755" target="_blank" >http://dx.doi.org/10.1080/03610926.2017.1422755</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/03610926.2017.1422755" target="_blank" >10.1080/03610926.2017.1422755</a>
Alternative languages
Result language
angličtina
Original language name
Multilevel maximum likelihood estimation with application to covariance matrices
Original language description
The asymptotic variance of the maximum likelihood estimate is proved to decrease when the maximization is restricted to a subspace that contains the true parameter value. Maximum likelihood estimation allows a systematic fitting of covariance models to the sample, which is important in data assimilation. The hierarchical maximum likelihood approach is applied to the spectral diagonal covariance model with different parameterizations of eigenvalue decay, and to the sparse inverse covariance model with specified parameter values on different sets of nonzero entries. It is shown computationally that using smaller sets of parameters can decrease the sampling noise in high dimension substantially.
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/GA13-34856S" target="_blank" >GA13-34856S: Advanced random field methods in data assimilation for short-term weather prediction</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Communications in Statistics - Theory and Methods
ISSN
0361-0926
e-ISSN
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Volume of the periodical
48
Issue of the periodical within the volume
4
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
909-925
UT code for WoS article
000468073600010
EID of the result in the Scopus database
2-s2.0-85041122146