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001			292238
003			MX-SnUAN
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007			cr nn 008mamaa
008			150903s2013 gw | o |||| 0|eng d
020			_a9783319015057 _99783319015057
024	7		_a10.1007/9783319015057 _2doi
035			_avtls000346018
039		9	_a201509030911 _bVLOAD _c201405050328 _dVLOAD _y201402070847 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA276-280
100	1		_aErcan, Ali. _eautor _9323873
245	1	0	_aLong-Range Dependence and Sea Level Forecasting / _cby Ali Ercan, M. Levent Kavvas, Rovshan K. Abbasov.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2013.
300			_av, 51 páginas 21 ilustraciones, 6 ilustraciones en color. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aSpringerBriefs in Statistics, _x2191-544X
500			_aSpringer eBooks
505	0		_a1. Introduction -- 2. Long-Range Dependence and ARFIMA Models -- 3. Forecasting, Confidence Band Estimation and Updating -- 4.Case Study I: Caspian Sea Level -- 5.Case Study II: Sea Level Change at Peninsular Malaysia and Sabah-Sarawak -- 6. Summary and Conclusions -- 7. References.
520			_aThis study shows that the Caspian Sea level time series possess long range dependence even after removing linear trends, based on analyses of the Hurst statistic, the sample autocorrelation functions, and the periodogram of the series. Forecasting performance of ARMA, ARIMA, ARFIMA and Trend Line-ARFIMA (TL-ARFIMA) combination models are investigated. The forecast confidence bands and the forecast updating methodology, provided for ARIMA models in the literature, are modified for the ARFIMA models. Sample autocorrelation functions are utilized to estimate the differencing lengths of the ARFIMA models. The confidence bands of the forecasts are estimated using the probability densities of the residuals without assuming a known distribution. There are no long-term sea level records for the region of Peninsular Malaysia and Malaysia’s Sabah-Sarawak northern region of Borneo Island. In such cases the Global Climate Model (GCM) projections for the 21st century can be downscaled to the Malaysia region by means of regression techniques, utilizing the short records of satellite altimeters in this region against the GCM projections during a mutual observation period. This book will be useful for engineers and researchers working in the areas of applied statistics, climate change, sea level change, time series analysis, applied earth sciences, and nonlinear dynamics.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aKavvas, M. Levent. _eautor _9323874
700	1		_aAbbasov, Rovshan K. _eautor _9323875
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9783319015040
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-319-01505-7 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c292238 _d292238