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EN
The Oseledec theorem (1968) and the theorems given in the paper Eckmann, Ruelle (1985) show the Lyapunov exponents exist for almost all the points in the state space of a dynamical system, and they are constant for almost all points in the basin of attraction of the attractor of dynamical system. However, the above-mentioned theorem applies only to deterministic systems. The Oseledec theorem provides the stability of the largest Lyapunov exponent regardless of the number of observations for the time series generated by deterministic chaotic system. While for the time series generated by a stochastic system, increase the number of observations in a series will cause change in the value of the largest Lyapunov exponent. In this paper researched the effect of the number of observations of the time series on the value of largest Lyapunov exponent. In addition, the stability of the largest Lyapunov exponent was examined in the time series after random noise reduction procedure.
PL
W pracy na podstawie znanego twierdzenia centralnego Lapunowa jest wyprowadzany rozkład graniczny prawdopodobieństwa znanej statystyki Horvitza-Thompsona (HT). Okazało się, że jeśli określane przez plan losowania Poissona prawdopodobieństwa wylosowania do próby poszczególnych elementów populacji spełniają pewne założenia oraz rozmiar populacji rośnie nieograniczenie, to rozkład standardowej postaci statystyki HT zmierza do rozkładu normalnego standardowego. Taki sam wynik otrzymano przy dodatkowym założeniu narzuconym na prawdopodobieństwa wylosowania elementów populacji do próby, gdy w standardowej postaci statystyki HT jej odchylenie standardowe zastąpimy przez pierwiastek z nieobciążonego estymatora tej wariancji. Rezultaty pracy znajdują zastosowania np. w pewnych typach badań ankietowych, a w szczególności internetowych, wykorzystujących wnioskowanie statystyczne, czyli estymację przedziałową lub testowanie hipotez statystycznych.
EN
In this paper has been researched the effect of random noise reduction on the accuracy of forecasts of economic time series obtained using the largest Lyapunov exponent method (LEM). The aim of the article was to compare the prediction errors obtained by LEM for the series before and after the random noice reduction and the time series filtred by models ARMA. The nearest neighbors method was used to reduce random noise in economic time series.
EN
The research on price volatility in the capital market, which have been conducted for many years led to create a wide variety of analytical approaches. One of them is developed by T. Vage coherent market hypothesis. To describe the volatility in the stock market T. Vaga proposed nonlinear statistical model based on the theory of social imitation. This model assumes transitions between different states of the capital market: from a state of effective market to a state of chaotic and coherent market. In this paper Vaga's hypothesis will be verified in the Polish capital market. Detailed research will be states of chaotic market, which will be verified by Lyapunov exponents.
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