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001 9781003107293
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005 20220509193109.0
006 m o d
007 cr |||||||||||
008 210203s2021 flua fob 001 0 eng d
040 _aOCoLC-P
_beng
_erda
_epn
_cOCoLC-P
020 _a1000392392
020 _a9781003107293
_q(electronic bk.)
020 _a100310729X
_q(electronic bk.)
020 _a9781000392401
_q(electronic bk. : EPUB)
020 _a1000392406
_q(electronic bk. : EPUB)
020 _a9781000392395
_q(electronic bk. : PDF)
035 _a(OCoLC)1245420122
035 _a(OCoLC-P)1245420122
050 4 _aQA278
072 7 _aMAT
_x029050
_2bisacsh
072 7 _aMAT
_x029020
_2bisacsh
072 7 _aPBT
_2bicssc
082 0 4 _a519.5/35
_223
100 1 _aBolla, Marianna,
_eauthor.
245 1 0 _aMultidimensional stationary time series
_bdimension reduction and prediction /
_cMarianna Bolla, Tamas Szabados.
264 1 _aBoca Raton :
_bChapman & Hall/CRC,
_c2021.
300 _a1 online resource
_billustrations (black and white)
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
520 _aThis book gives a brief survey of the theory of multidimensional (multivariate), weakly stationary time series, with emphasis on dimension reduction and prediction. Understanding the covered material requires a certain mathematical maturity, a degree of knowledge in probability theory, linear algebra, and also in real, complex and functional analysis. For this, the cited literature and the Appendix contain all necessary material. The main tools of the book include harmonic analysis, some abstract algebra, and state space methods: linear time-invariant filters, factorization of rational spectral densities, and methods that reduce the rank of the spectral density matrix. * Serves to find analogies between classical results (Cramer, Wold, Kolmogorov, Wiener, Klmn, Rozanov) and up-to-date methods for dimension reduction in multidimensional time series.* Provides a unified treatment for time and frequency domain inferences by using machinery of complex and harmonic analysis, spectral and Smith--McMillan decompositions. Establishes analogies between the time and frequency domain notions and calculations.* Discusses the Wold's decomposition and the Kolmogorov's classification together, by distinguishing between different types of singularities. Understanding the remote past helps us to characterize the ideal situation where there is a regular part at present. Examples and constructions are also given. * Establishes a common outline structure for the state space models, prediction, and innovation algorithms with unified notions and principles, which is applicable to real-life high frequency time series. It is an ideal companion for graduate students studying the theory of multivariate time series and researchers working in this field.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aMultivariate analysis.
650 0 _aDimension reduction (Statistics)
650 7 _aMATHEMATICS / Probability & Statistics / Multivariate Analysis
_2bisacsh
700 1 _aSzabados, Tamás,
_eauthor.
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9781003107293
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999 _c129737
_d129737