Maximum likelihood estimators in time series

Autor:	Tritová, Hana
Přispěvatelé:	Pawlas, Zbyněk, Zikmundová, Markéta
Jazyk:	čeština
Rok vydání:	2011
Předmět:	autoregresní posloupnost maximum likelihood estimation autoregressive process time series časová řada maximálně věrohodný odhad moving average process posloupnost klouzavých součtů
Popis:	The thesis deals with maximum likelihood estimators in time series. The reader becomes familiar with three important models for time series: autoregressive model (AR), moving average model (MA) and autoregressive moving average (ARMA). Thereafter he can find out the form of their main characteristics, e.g. population mean and variance. Then there is the derivation of parameter estimates - generally and for mentioned models of times series. There are also stated two other methods for finding estimators of AR(1) and MA(1) parameters - method of moments and least squares method. The end is dedicated to examples which compares all three methods.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::2e4a9e1bc0e168824e92453c50332cdd http://www.nusl.cz/ntk/nusl-314577 Zobrazit plný text záznamu