MARC Record from harvard_bibliographic_metadata

LEADER: 03015nam a22004455a 4500
001 013699737-6
005 20130712192652.0
008 130514s2013 gw | s ||0| 0|eng d
020 $a9783642355127
020 $a9783642355127
020 $a9783642355110
024 7 $a10.1007/978-3-642-35512-7$2doi
035 $a(Springer)9783642355127
040 $aSpringer
050 4 $aQA276-280
072 7 $aPBT$2bicssc
072 7 $aMAT029000$2bisacsh
082 04 $a519.5$223
100 1 $aBeran, Jan.
245 10 $aLong-Memory Processes :$bProbabilistic Properties and Statistical Methods /$cby Jan Beran, Yuanhua Feng, Sucharita Ghosh, Rafal Kulik.
260 $aBerlin, Heidelberg :$bSpringer Berlin Heidelberg :$bImprint: Springer,$c2013.
300 $aXVII, 884 p. 89 illus., 60 illus. in color.$bdigital.
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 10 $aStatistics.
650 0 $aDistribution (Probability theory)
650 0 $aStatistics.
650 0 $aMathematical statistics.
650 0 $aEconomics$xStatistics.
650 24 $aStatistical Theory and Methods.
650 24 $aProbability Theory and Stochastic Processes.
650 24 $aStatistics for Business/Economics/Mathematical Finance/Insurance.
650 24 $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 24 $aStatistics for Life Sciences, Medicine, Health Sciences.
700 1 $aFeng, Yuanhua.
700 1 $aGhosh, Sucharita.
700 1 $aKulik, Rafal.
776 08 $iPrinted edition:$z9783642355110
988 $a20130604
906 $0VEN