000 03375nam a22005055i 4500
001 978-81-322-0628-6
003 DE-He213
005 20140220083334.0
007 cr nn 008mamaa
008 120523s2012 ii | s |||| 0|eng d
020 _a9788132206286
_9978-81-322-0628-6
024 7 _a10.1007/978-81-322-0628-6
_2doi
050 4 _aQA276-280
072 7 _aUFM
_2bicssc
072 7 _aCOM077000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aKundu, Debasis.
_eauthor.
245 1 0 _aStatistical Signal Processing
_h[electronic resource] :
_bFrequency Estimation /
_cby Debasis Kundu, Swagata Nandi.
264 1 _aIndia :
_bSpringer India :
_bImprint: Springer,
_c2012.
300 _aXVII, 132 p. 21 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Statistics,
_x2191-544X
505 0 _a1 Introduction -- 2 Notations and Preliminaries -- 3 Estimation of Frequencies -- 4 Asymptotic Properties -- 5 Estimating the Number of Components -- 6 Real Data Example -- 7 Multidimensional Models -- 8 Related Models -- References -- Index.
520 _aSignal processing may broadly be considered to involve the recovery of information from physical observations. The received signal is usually disturbed by thermal, electrical, atmospheric or intentional interferences. Due to the random nature of the signal, statistical techniques play an important role in analyzing the signal. Statistics is also used in the formulation of the appropriate models to describe the behavior of the system, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Statistical signal processing basically refers to the analysis of random signals using appropriate statistical techniques. The main aim of this book is to introduce different signal processing models which have been used in analyzing periodic data, and different statistical and computational issues involved in solving them. We discuss in detail the sinusoidal frequency model which has been used extensively in analyzing periodic data occuring in various fields. We have tried to introduce different associated models and higher dimensional statistical signal processing models which have been further discussed in the literature. Different real data sets have been analyzed to illustrate how different models can be used in practice. Several open problems have been indicated for future research.
650 0 _aStatistics.
650 0 _aAlgorithms.
650 0 _aMathematical statistics.
650 0 _aEngineering mathematics.
650 1 4 _aStatistics.
650 2 4 _aStatistics and Computing/Statistics Programs.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aAlgorithms.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
700 1 _aNandi, Swagata.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9788132206279
830 0 _aSpringerBriefs in Statistics,
_x2191-544X
856 4 0 _uhttp://dx.doi.org/10.1007/978-81-322-0628-6
912 _aZDB-2-SMA
999 _c104062
_d104062