MARC Record from harvard_bibliographic_metadata

LEADER: 03015nam a22004335a 4500
001 013840091-1
005 20131206201454.0
008 121227s2000 xxu| s ||0| 0|eng d
020 $a9781461212621
020 $a9781461212621
020 $a9781461270676
024 7 $a10.1007/978-1-4612-1262-1$2doi
035 $a(Springer)9781461212621
040 $aSpringer
050 4 $aQA276-280
072 7 $aPBT$2bicssc
072 7 $aMAT029000$2bisacsh
082 04 $a519.5$223
100 1 $aRosenblatt, Murray,$eauthor.
245 10 $aGaussian and Non-Gaussian Linear Time Series and Random Fields /$cby Murray Rosenblatt.
264 1 $aNew York, NY :$bSpringer New York :$bImprint: Springer,$c2000.
300 $aXIII, 247 p.$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
505 0 $aReversibility and Identifiability -- Minimum Phase Estimation -- Homogeneous Gaussian Random Fields -- Cumulants, Mixing and Estimation for Gaussian Fields -- Prediction for Minimum and Nonminimum Phase Models -- The Fluctuation of the quasi-Gaussian Likelihood -- Random Fields -- Estimation for Possibly Nonminimum Phase Schemes.
520 $aThe book is concerned with linear time series and random fields in both the Gaussian and especially the non-Gaussian context. The principal focus is on autoregressive moving average models and analogous random fields. Probabilistic and statistical questions are both discussed. The Gaussian models are contrasted with noncausal or noninvertible (nonminimum phase) non-Gaussian models which can have a much richer structure than Gaussian models. The book deals with problems of prediction (which can have a nonlinear character) and estimation. New results for nonminimum phase non-Gaussian processes are exposited and open questions are noted. The book is intended as a text for graduate students in statistics, mathematics, engineering, the natural sciences and economics. An initial background in probability theory and statistics is suggested. Notes on background, history and open problems are given at the end of the book. Murray Rosenblatt is Professor of Mathematics at the University of California, San Diego. He was a Guggenheim Fellow in 1965 and 1972 and is a member of the National Academy of Sciences, U.S.A. He is the author of Random Processes (1962), Markov Processes: Structure and Asymptotic Behavior (1971), Stationary Sequences and Random Fields (1985), and Stochastic Curve Estimation (1991).
650 10 $aStatistics.
650 0 $aDistribution (Probability theory)
650 0 $aStatistics.
650 0 $aMathematical statistics.
650 24 $aStatistical Theory and Methods.
650 24 $aProbability Theory and Stochastic Processes.
776 08 $iPrinted edition:$z9781461270676
830 0 $aSpringer Series in Statistics.
988 $a20131119
906 $0VEN