000 03349nam a22005057a 4500
001 sulb-eb0024514
003 BD-SySUS
005 20160413122443.0
007 cr nn 008mamaa
008 130514s2013 gw | s |||| 0|eng d
020 _a9783642355127
_9978-3-642-35512-7
024 7 _a10.1007/978-3-642-35512-7
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aBeran, Jan.
_eauthor.
245 1 0 _aLong-Memory Processes
_h[electronic resource] :
_bProbabilistic Properties and Statistical Methods /
_cby Jan Beran, Yuanhua Feng, Sucharita Ghosh, Rafal Kulik.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aXVII, 884 p. 89 illus., 60 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aDefinition of Long Memory -- Origins and Generation of Long Memory -- Mathematical Concepts -- Limit Theorems -- Statistical Inference for Stationary Processes -- Statistical Inference for Nonlinear Processes -- Statistical Inference for Nonstationary Processes -- Forecasting -- Spatial and Space-Time Processes -- Resampling -- Function Spaces -- Regularly Varying Functions -- Vague Convergence -- Some Useful Integrals -- Notation and Abbreviations.
520 _aLong-memory processes are known to play an important part in many areas of science and technology, including physics, geophysics, hydrology, telecommunications, economics, finance, climatology, and network engineering. In the last 20 years enormous progress has been made in understanding the probabilistic foundations and statistical principles of such processes. This book provides a timely and comprehensive review, including a thorough discussion of mathematical and probabilistic foundations and statistical methods, emphasizing their practical motivation and mathematical justification. Proofs of the main theorems are provided and data examples illustrate practical aspects. This book will be a valuable resource for researchers and graduate students in statistics, mathematics, econometrics and other quantitative areas, as well as for practitioners and applied researchers who need to analyze data in which long memory, power laws, self-similar scaling or fractal properties are relevant.
650 0 _aStatistics.
650 0 _aProbabilities.
650 1 4 _aStatistics.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
700 1 _aFeng, Yuanhua.
_eauthor.
700 1 _aGhosh, Sucharita.
_eauthor.
700 1 _aKulik, Rafal.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642355110
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-35512-7
912 _aZDB-2-SMA
942 _2Dewey Decimal Classification
_ceBooks
999 _c46606
_d46606