000			04251nam a22003855i 4500
001			286164
003			MX-SnUAN
005			20160429154447.0
007			cr nn 008mamaa
008			150903s2010 xxu| o |||| 0|eng d
020			_a9781441906618 _99781441906618
024	7		_a10.1007/9781441906618 _2doi
035			_avtls000338066
039		9	_a201509030810 _bVLOAD _c201404300339 _dVLOAD _y201402060901 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA276-280
100	1		_aBai, Zhidong. _eautor _9234848
245	1	0	_aSpectral Analysis of Large Dimensional Random Matrices / _cby Zhidong Bai, Jack W. Silverstein.
264		1	_aNew York, NY : _bSpringer New York, _c2010.
300			_axvI, 552 páginas _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aSpringer Series in Statistics, _x0172-7397
500			_aSpringer eBooks
505	0		_aWigner Matrices and Semicircular Law -- Sample Covariance Matrices and the Mar?enko-Pastur Law -- Product of Two Random Matrices -- Limits of Extreme Eigenvalues -- Spectrum Separation -- Semicircular Law for Hadamard Products -- Convergence Rates of ESD -- CLT for Linear Spectral Statistics -- Eigenvectors of Sample Covariance Matrices -- Circular Law -- Some Applications of RMT.
520			_aThe aim of the book is to introduce basic concepts, main results, and widely applied mathematical tools in the spectral analysis of large dimensional random matrices. The core of the book focuses on results established under moment conditions on random variables using probabilistic methods, and is thus easily applicable to statistics and other areas of science. The book introduces fundamental results, most of them investigated by the authors, such as the semicircular law of Wigner matrices, the Marcenko-Pastur law, the limiting spectral distribution of the multivariate F matrix, limits of extreme eigenvalues, spectrum separation theorems, convergence rates of empirical distributions, central limit theorems of linear spectral statistics, and the partial solution of the famous circular law. While deriving the main results, the book simultaneously emphasizes the ideas and methodologies of the fundamental mathematical tools, among them being: truncation techniques, matrix identities, moment convergence theorems, and the Stieltjes transform. Its treatment is especially fitting to the needs of mathematics and statistics graduate students and beginning researchers, having a basic knowledge of matrix theory and an understanding of probability theory at the graduate level, who desire to learn the concepts and tools in solving problems in this area. It can also serve as a detailed handbook on results of large dimensional random matrices for practical users. This second edition includes two additional chapters, one on the authors' results on the limiting behavior of eigenvectors of sample covariance matrices, another on applications to wireless communications and finance. While attempting to bring this edition up-to-date on recent work, it also provides summaries of other areas which are typically considered part of the general field of random matrix theory. Zhidong Bai is a professor of the School of Mathematics and Statistics at Northeast Normal University and Department of Statistics and Applied Probability at National University of Singapore. He is a Fellow of the Third World Academy of Sciences and a Fellow of the Institute of Mathematical Statistics. Jack W. Silverstein is a professor in the Department of Mathematics at North Carolina State University. He is a Fellow of the Institute of Mathematical Statistics.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aSilverstein, Jack W. _eautor _9314698
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781441906601
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4419-0661-8 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c286164 _d286164