Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Dreižienė, Lina"'
Autor:
Dreiziene, Lina, Ducinskas, Kestutis
Publikováno v:
In Spatial Statistics March 2020 35
Autor:
Dreižienė, Lina, Dučinskas, Kęstutis
Publikováno v:
In Procedia Environmental Sciences 2015 26:78-81
Autor:
Dučinskas, Kęstutis1,2, Dreižienė, Lina1,3 l.dreiziene@gmail.com
Publikováno v:
Lithuanian Journal of Statistics / Lietuvos Statistikos Darbai. 2021, Vol. 60, p1-7. 7p.
Autor:
Ducinskas, Kestutis, Dreižiene, Lina
Publikováno v:
In Procedia Environmental Sciences 2011 7:212-217
Autor:
Dučinskas, Kęstutis1,2, Dreižienė, Lina1,2 l.dreiziene@gmail.com
Publikováno v:
Journal of Classification. Oct2018, Vol. 35 Issue 3, p422-436. 15p.
Autor:
Dučinskas, Kęstutis, Dreižienė, Lina
Publikováno v:
Lietuvos Matematikos Rinkinys, Vol 47, Iss spec. (2021)
Lietuvos matematikos rinkinys : Lietuvos matematikų draugijos XLVIII konferencijos mokslo darbai, Vilnius, 2007, t. 47, spec. Nr., p. 359-363
Lietuvos matematikos rinkinys : Lietuvos matematikų draugijos XLVIII konferencijos mokslo darbai, Vilnius, 2007, t. 47, spec. Nr., p. 359-363
Paper deals with statistical classification of spatial data as a part of widely applicable statistical approach to pattern recognition. Error rates in supervised classification of Gaussian random field observation into one of two populations specifie
Autor:
Dučinskas, Kęstutis, Dreižienė, Lina
Publikováno v:
Lietuvos statistikos darbai, Vilnius : Vilniaus universiteto leidykla, 2021, t. 60, Nr. 1, p. 1-7
Bayes multiclass classification of spatial Gaussian data following the universal kriging model is considered. The closed-form expressions for the maximum likelihood (ML) estimator of regression parameters and the actual error rate (AER) in terms of s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::3ac8d9ef6c6434ab9b53e2866ccc4a4f
https://repository.vu.lt/VU:ELABAPDB123689079&prefLang=en_US
https://repository.vu.lt/VU:ELABAPDB123689079&prefLang=en_US
Akademický článek
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Autor:
Dreižienė, Lina
The thesis is devoted to the linear discriminant analysis of spatially correlated data. The presence of spatial correlation violates the assumption of independent observations which is the background for many classical statistical methods. Therefore,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::b128264e29fcf41321698e6e6329a680
https://repository.vu.lt/VU:ELABAETD34791998&prefLang=en_US
https://repository.vu.lt/VU:ELABAETD34791998&prefLang=en_US
Publikováno v:
In Statistics and Probability Letters March 2015 98:107-114