000 04018nam a22005175i 4500
001 978-3-642-24127-7
003 DE-He213
005 20140220083303.0
007 cr nn 008mamaa
008 111021s2012 gw | s |||| 0|eng d
020 _a9783642241277
_9978-3-642-24127-7
024 7 _a10.1007/978-3-642-24127-7
_2doi
050 4 _aQA273.A1-274.9
050 4 _aQA274-274.9
072 7 _aPBT
_2bicssc
072 7 _aPBWL
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.2
_223
100 1 _aJacod, Jean.
_eauthor.
245 1 0 _aDiscretization of Processes
_h[electronic resource] /
_cby Jean Jacod, Philip Protter.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2012.
300 _aXVI, 596 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStochastic Modelling and Applied Probability,
_x0172-4568 ;
_v67
505 0 _aPart I Introduction and Preliminary Material -- 1.Introduction -- 2.Some Prerequisites -- Part II The Basic Results -- 3.Laws of Large Numbers: the Basic Results -- 4.Central Limit Theorems: Technical Tools -- 5.Central Limit Theorems: the Basic Results -- 6.Integrated Discretization Error -- Part III More Laws of Large Numbers -- 7.First Extension: Random Weights -- 8.Second Extension: Functions of Several Increments -- 9.Third Extension: Truncated Functionals -- Part IV Extensions of the Central Limit Theorems -- 10.The Central Limit Theorem for Random Weights -- 11.The Central Limit Theorem for Functions of a Finite Number of Increments -- 12.The Central Limit Theorem for Functions of an Increasing Number of Increments -- 13.The Central Limit Theorem for Truncated Functionals -- Part V Various Extensions -- 14.Irregular Discretization Schemes. 15.Higher Order Limit Theorems -- 16.Semimartingales Contaminated by Noise -- Appendix -- References -- Assumptions -- Index of Functionals -- Index.
520 _aIn applications, and especially in mathematical finance, random time-dependent events are often modeled as stochastic processes. Assumptions are made about the structure of such processes, and serious researchers will want to justify those assumptions through the use of data.  As statisticians are wont to say, “In God we trust; all others must bring data.”   This book establishes the theory of how to go about estimating not just scalar parameters about a proposed model, but also the underlying structure of the model itself.  Classic statistical tools are used: the law of large numbers, and the central limit theorem. Researchers have recently developed creative and original methods to use these tools in sophisticated (but highly technical) ways to reveal new details about the underlying structure. For the first time in book form, the authors present these latest techniques, based on research from the last 10 years. They include new findings.   This book will be of special interest to researchers, combining the theory of mathematical finance with its investigation using market data, and it will also prove to be useful in a broad range of applications, such as to mathematical biology, chemical engineering, and physics.
650 0 _aMathematics.
650 0 _aDistribution (Probability theory).
650 0 _aEconomics
_xStatistics.
650 0 _aEconometrics.
650 1 4 _aMathematics.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
650 2 4 _aEconometrics.
700 1 _aProtter, Philip.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642241260
830 0 _aStochastic Modelling and Applied Probability,
_x0172-4568 ;
_v67
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-24127-7
912 _aZDB-2-SMA
999 _c102260
_d102260