000 03417nam a22004935i 4500
001 978-1-4614-3655-3
003 DE-He213
005 20140220083248.0
007 cr nn 008mamaa
008 120507s2012 xxu| s |||| 0|eng d
020 _a9781461436553
_9978-1-4614-3655-3
024 7 _a10.1007/978-1-4614-3655-3
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aHorváth, Lajos.
_eauthor.
245 1 0 _aInference for Functional Data with Applications
_h[electronic resource] /
_cby Lajos Horváth, Piotr Kokoszka.
264 1 _aNew York, NY :
_bSpringer New York,
_c2012.
300 _aXIV, 422p. 66 illus., 9 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Series in Statistics,
_x0172-7397 ;
_v200
505 0 _aIndependent functional observations -- The functional linear model -- Dependent functional data -- References -- Index.
520 _aThis book presents recently developed statistical methods and theory required for the application of the tools of functional data analysis to problems arising in geosciences, finance, economics and biology. It is concerned with inference based on second order statistics, especially those related to the functional principal component analysis. While it covers inference for independent and identically distributed functional data, its distinguishing feature is an in depth coverage of dependent functional data structures, including functional time series and spatially indexed functions. Specific inferential problems studied include two sample inference, change point analysis, tests for dependence in data and model residuals and functional prediction. All procedures are described algorithmically, illustrated on simulated and real data sets, and supported by a complete asymptotic theory. The book can be read at two levels. Readers interested primarily in methodology will find detailed descriptions of the methods and examples of their application. Researchers interested also in mathematical foundations will find carefully developed theory. The organization of the chapters makes it easy for the reader to choose an appropriate focus. The book introduces the requisite, and frequently used, Hilbert space formalism in a systematic manner. This will be useful to graduate or advanced undergraduate students seeking a self-contained introduction to the subject. Advanced researchers will find novel asymptotic arguments.
650 0 _aStatistics.
650 0 _aMathematical statistics.
650 0 _aEconomics
_xStatistics.
650 1 4 _aStatistics.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aStatistics, general.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
700 1 _aKokoszka, Piotr.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461436546
830 0 _aSpringer Series in Statistics,
_x0172-7397 ;
_v200
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-3655-3
912 _aZDB-2-SMA
999 _c101417
_d101417