000 03472nam a22003735i 4500
001 293883
003 MX-SnUAN
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007 cr nn 008mamaa
008 150903s2014 gw | o |||| 0|eng d
020 _a9783319031491
_99783319031491
024 7 _a10.1007/9783319031491
_2doi
035 _avtls000346366
039 9 _a201509030915
_bVLOAD
_c201405050334
_dVLOAD
_y201402070856
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQA276-280
100 1 _aAhmed, S. Ejaz.
_eautor
_9326933
245 1 0 _aPenalty, Shrinkage and Pretest Strategies :
_bVariable Selection and Estimation /
_cby S. Ejaz Ahmed.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aIx, 115 páginas 6 ilustraciones
_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 _aSpringerBriefs in Statistics,
_x2191-544X
500 _aSpringer eBooks
505 0 _aPreface -- Estimation Strategies -- Improved Estimation Strategies in Normal and Poisson Models -- Pooling Data: Making Sense or Folly -- Estimation Strategies in Multiple Regression Models -- Estimation Strategies in Partially Linear Models -- Estimation Strategies in Poisson Regression Models.
520 _aThe objective of this book is to compare the statistical properties of penalty and non-penalty estimation strategies for some popular models.  Specifically, it considers the full model, submodel, penalty, pretest and shrinkage estimation techniques for three regression models before presenting the asymptotic properties of the non-penalty estimators and their asymptotic distributional efficiency comparisons.  Further, the risk properties of the non-penalty estimators and penalty estimators are explored through a Monte Carlo simulation study. Showcasing examples based on real datasets, the book will be useful for students and applied researchers in a host of applied fields. The book’s level of presentation and style make it accessible to a broad audience. It offers clear, succinct expositions of each estimation strategy.  More importantly, it clearly describes how to use each estimation strategy for the problem at hand.  The book is largely self-contained, as are the individual chapters, so that anyone interested in a particular topic or area of application may read only that specific chapter. The book is specially designed for graduate students who want to understand the foundations and concepts underlying penalty and non-penalty estimation and its applications. It is well-suited as a textbook for senior undergraduate and graduate courses surveying penalty and non-penalty estimation strategies, and can also be used as a reference book for a host of related subjects, including courses on meta-analysis. Professional statisticians will find this book to be a valuable reference work, since nearly all chapters are self-contained.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9783319031484
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-319-03149-1
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c293883
_d293883