@misc{Flimmel_Samuel_Comparison_2017,
 author={Flimmel, Samuel and Čamaj, Matej and Malá, Ivana and Procházka, Jiří},
 identifier={DOI: 10.15611/amse.2017.20.10},
 year={2017},
 rights={Wszystkie prawa zastrzeżone (Copyright)},
 publisher={Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu},
 description={20-th AMSE. Applications of Mathematics and Statistics in Economics. International Scientific Conference: Szklarska Poręba, 30 August- 3 September 2017. Conference Proceedings Full Text Papers, s. 133-144},
 language={eng},
 abstract={In financial markets, we are facing big data problems. Loads of information are stored almost every second, but usually standard methods have problems to process them all. With growing number of observations, the probability of outlier presence also rises. That is the reason of increase in importance to work with sufficiently robust methods. As it is known, standard methods are not able to work correctly with outliers and consequently standard estimates are usually biased. ARMA processes are frequently used in financial mathematics and one of the important steps is to estimate the order of a given process. Usually it is the second step in Box-Jenkins method after solving stationarity and seasonality. In this paper we present robust methods for ARMA order estimating and we compare them using a simulation study. For the simulation study we are using the R statistical software},
 title={Comparison of Robust Methods for ARMA Order Estimation},
 type={materiały konferencyjne},
 keywords={robust methods, ARMA process, ARMA order estimation},
}