9781402012976 - Nonstationarities in Hydrologic and Environmental Time Series (Water Science and Technology Library, 45, Band 45) von Rao, A.R.; Hamed, K.H.; Huey-Long Chen (5 Ergebnisse)

2003th ed. 17 x 25 cm. 392 pages. HC Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.

Gebunden. Zustand: New. Conventionally, time series have been studied either in the time domain or the frequency domain. The representation of a signal in the time domain is localized in time, i.e . the value of the signal at each instant in time is well defined . However, the tim.

Hardcover. Zustand: Brand New. 2003 edition. 365 pages. 9.90x6.70x0.75 inches. In Stock.

Zustand: New. Presents a number of techniques that have been discussed in the literature concerning the investigation of stationarity, linearity and Gaussianity of hydrologic and environmental times series. These techniques cover different approaches for assessing nonstationarity, ranging from time domain analysis, to frequency domain analysis, and others. Series: Water Science and Technology Library. Num Pages: 392 pages, biography. BIC Classification: PBT; RBKC; RBKF. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 237 x 163 x 30. Weight in Grams: 726. . 2003. Hardback. . . . . Books ship from the US and Ireland.

Buch. Zustand: Neu. Neuware - Conventionally, time series have been studied either in the time domain or the frequency domain. The representation of a signal in the time domain is localized in time, i.e . the value of the signal at each instant in time is well defined . However, the time representation of a signal is poorly localized in frequency , i.e. little information about the frequency content of the signal at a certain frequency can be known by looking at the signal in the time domain . On the other hand, the representation of a signal in the frequency domain is well localized in frequency, but is poorly localized in time, and as a consequence it is impossible to tell when certain events occurred in time. In studying stationary or conditionally stationary processes with mixed spectra , the separate use of time domain and frequency domain analyses is sufficient to reveal the structure of the process . Results discussed in the previous chapters suggest that the time series analyzed in this book are conditionally stationary processes with mixed spectra. Additionally, there is some indication of nonstationarity, especially in longer time series.