Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników

Znaleziono wyników: 2

Liczba wyników na stronie
Pierwsza strona wyników Pięć stron wyników wstecz Poprzednia strona wyników Strona / 1 Następna strona wyników Pięć stron wyników wprzód Ostatnia strona wyników

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych:  stationarity
help Sortuj według:

help Ogranicz wyniki do:
Pierwsza strona wyników Pięć stron wyników wstecz Poprzednia strona wyników Strona / 1 Następna strona wyników Pięć stron wyników wprzód Ostatnia strona wyników
We have analyzed the statistical characteristics of riverflow variability in the Odra River basin in southwestern Poland. In particular, we have examined the daily discharge time series recorded at 15 sites from November 1971 to October 2006. The skewness and kurtosis values of the time series are computed to determine if the empirical distribution of the data follows a normal distribution. The empirical distributions of all the time series are found to be non-Gaussian. The kurtosis values are interpreted in terms of intermittency, and together with skewness they are found to be significantly correlated with morphometric properties of the subbasins. In addition, several theoretical probability distributions are fitted to the riverflow data at each site. Among them, the 5-parameter Wakeby distribution is found to provide the best overall fit. Subsequently, the Wakeby distribution is used to calculate the return periods. Finally, the trend and stationarity around a trend of the various riverflow time series are assessed using the Cox-Stuart and Phillips-Perron (Dickey-Fuller) statistical tests. A decreasing trend is found in the daily discharge data at all sites, but there is no evidence of nonstationarity around the trend over the time span of the data record. A good understanding of the statistical characteristics of riverflow fluctuations in the Odra River basin is essential for water resources planning and management, including flood control and prediction in SW Poland.
We describe nonlinear deterministic versus stochastic methodology, their applications to EEG research and the neurophysiological background underlying both approaches. Nonlinear methods are based on the concept of attractors in phase space. This concept on the one hand incorporates the idea of an autonomous (stationary) system, on the other hand implicates the investigation of a long time evolution. It is an unresolved problem in nonlinear EEG research that nonlinear methods per se give no feedback about the stationarity aspect. Hence, we introduce a combined strategy utilizing both stochastic and nonlinear deterministic methods. We propose, in a first step to segment the EEG time series into piecewise quasi-stationary epochs by means of nonparametric change point analysis. Subsequently, nonlinear measures can be estimated with higher confidence for the segmented epochs fullfilling the stationarity condition.
Pierwsza strona wyników Pięć stron wyników wstecz Poprzednia strona wyników Strona / 1 Następna strona wyników Pięć stron wyników wprzód Ostatnia strona wyników
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.