Comparison of mixture-based classification with the data-dependent pointer model for various types of components

0462164 - ÚTIA 2017 CZ eng V - Výzkumná zpráva
Likhonina, Raissa - Suzdaleva, Evgenia - Nagy, Ivan
Comparison of mixture-based classification with the data-dependent pointer model for various types of components.
Praha: ÚTIA AV ČR, 2016. 59 s. Research Report, 2355.
Grant CEP: GA ČR GA15-03564S
Institucionální podpora: RVO:67985556
Klíčová slova: mixture-based classification * data-dependent pointer * recurisive mixture estimation
Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
http://library.utia.cas.cz/separaty/2016/ZS/suzdaleva-0462164.pdf

The presented report is devoted to the analysis of a data-dependent pointer model, whether it brings some advantages in comparison with a data-independent pointer model at simulation and estimation of components referring to different types of distribution, including categorical, uniform, exponential and state-space components for a dynamic data-dependent model, and normal components for a static data-dependent pointer model.
Trvalý link: http://hdl.handle.net/11104/0262264