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020 _a9783319031491
_9978-3-319-03149-1
024 7 _a10.1007/978-3-319-03149-1
_2doi
035 _ato000542427
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aAhmed, S. Ejaz.
_eauthor.
_9449155
245 1 0 _aPenalty, Shrinkage and Pretest Strategies
_helectronic resource
_bVariable Selection and Estimation /
_cby S. Ejaz Ahmed.
260 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aIX, 115 p. 6 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aSpringerBriefs in Statistics,
_x2191-544X
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.
650 0 _aStatistics.
_9124796
650 0 _aMathematical statistics.
_9566264
650 1 4 _aStatistics.
_9124796
650 2 4 _aStatistical Theory and Methods.
_9303276
650 2 4 _aStatistics and Computing/Statistics Programs.
_9303277
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
830 0 _aSpringerBriefs in Statistics,
_9446803
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-03149-1
912 _aZDB-2-SMA
999 _c400571