000			04141nam a22004335i 4500
001			978-1-4419-5941-6
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007			cr nn 008mamaa
008			100623s2010 xxu| s |||| 0|eng d
020			_a9781441959416 _9978-1-4419-5941-6
024	7		_a10.1007/978-1-4419-5941-6 _2doi
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aSamaniego, Francisco J. _eauthor.
245	1	2	_aA Comparison of the Bayesian and Frequentist Approaches to Estimation _h[electronic resource] / _cby Francisco J. Samaniego.
264		1	_aNew York, NY : _bSpringer New York, _c2010.
300			_aXIII, 225 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		_aPoint Estimation from a Decision-Theoretic Viewpoint -- An Overview of the Frequentist Approach to Estimation -- An Overview of the Bayesian Approach to Estimation -- The Threshold Problem -- Comparing Bayesian and Frequentist Estimators of a Scalar Parameter -- Conjugacy, Self-Consistency and Bayesian Consensus -- Bayesian vs. Frequentist Shrinkage in Multivariate Normal Problems -- Comparing Bayesian and Frequentist Estimators under Asymmetric Loss -- The Treatment of Nonidentifiable Models -- Improving on Standard Bayesian and Frequentist Estimators -- Combining Data from “Related” Experiments -- Fatherly Advice.
520			_aThis monograph contributes to the area of comparative statistical inference. Attention is restricted to the important subfield of statistical estimation. The book is intended for an audience having a solid grounding in probability and statistics at the level of the year-long undergraduate course taken by statistics and mathematics majors. The necessary background on Decision Theory and the frequentist and Bayesian approaches to estimation is presented and carefully discussed in Chapters 1–3. The “threshold problem” -- identifying the boundary between Bayes estimators which tend to outperform standard frequentist estimators and Bayes estimators which don’t -- is formulated in an analytically tractable way in Chapter 4. The formulation includes a specific (decision-theory based) criterion for comparing estimators. The centerpiece of the monograph is Chapter 5 in which, under quite general conditions, an explicit solution to the threshold is obtained for the problem of estimating a scalar parameter under squared error loss. The six chapters that follow address a variety of other contexts in which the threshold problem can be productively treated. Included are treatments of the Bayesian consensus problem, the threshold problem for estimation problems involving of multi-dimensional parameters and/or asymmetric loss, the estimation of nonidentifiable parameters, empirical Bayes methods for combining data from ‘similar’ experiments and linear Bayes methods for combining data from ‘related’ experiments. The final chapter provides an overview of the monograph’s highlights and a discussion of areas and problems in need of further research. F. J. Samaniego is a Distinguished Professor of Statistics at the University of California, Davis. He served as Theory and Methods Editor of the Journal of the American Statistical Association (2003-05), was the 2004 recipient of the Davis Prize for Undergraduate Teaching and Scholarly Achievement, and is an elected Fellow of the ASA, the IMS and the RSS and an elected Member of the ISI.
650		0	_aStatistics.
650		0	_aMathematical statistics.
650	1	4	_aStatistics.
650	2	4	_aStatistical Theory and Methods.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781441959409
830		0	_aSpringer Series in Statistics, _x0172-7397
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4419-5941-6
912			_aZDB-2-SMA
999			_c110555 _d110555