000 | 03351nam a22004697a 4500 | ||
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001 | sulb-eb0022099 | ||
003 | BD-SySUS | ||
005 | 20160413122223.0 | ||
007 | cr nn 008mamaa | ||
008 | 130217s2013 xxu| s |||| 0|eng d | ||
020 |
_a9781461448181 _9978-1-4614-4818-1 |
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024 | 7 |
_a10.1007/978-1-4614-4818-1 _2doi |
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050 | 4 | _aQA276-280 | |
072 | 7 |
_aPBT _2bicssc |
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072 | 7 |
_aMAT029000 _2bisacsh |
|
082 | 0 | 4 |
_a519.5 _223 |
100 | 1 |
_aBoos, Dennis D. _eauthor. |
|
245 | 1 | 0 |
_aEssential Statistical Inference _h[electronic resource] : _bTheory and Methods / _cby Dennis D Boos, L. A Stefanski. |
264 | 1 |
_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013. |
|
300 |
_aXVII, 568 p. 34 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aSpringer Texts in Statistics, _x1431-875X ; _v120 |
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505 | 0 | _aRoles of Modeling in Statistical Inference.- Likelihood Construction and Estimation.- Likelihood-Based Tests and Confidence Regions.- Bayesian Inference.- Large Sample Theory: The Basics.- Large Sample Results for Likelihood-Based Methods.- M-Estimation (Estimating Equations).- Hypothesis Tests under Misspecification and Relaxed Assumptions .- Monte Carlo Simulation Studies .- Jackknife.- Bootstrap.- Permutation and Rank Tests.- Appendix: Derivative Notation and Formulas.- References.- Author Index.- Example Index -- R-code Index -- Subject Index. | |
520 | _aThis book is for students and researchers who have had a first year graduate level mathematical statistics course. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics like M-estimation, the jackknife, and the bootstrap. R code is woven throughout the text, and there are a large number of examples and problems. An important goal has been to make the topics accessible to a wide audience, with little overt reliance on measure theory. A typical semester course consists of Chapters 1-6 (likelihood-based estimation and testing, Bayesian inference, basic asymptotic results) plus selections from M-estimation and related testing and resampling methodology. Dennis Boos and Len Stefanski are professors in the Department of Statistics at North Carolina State. Their research has been eclectic, often with a robustness angle, although Stefanski is also known for research concentrated on measurement error, including a co-authored book on non-linear measurement error models. In recent years the authors have jointly worked on variable selection methods. . | ||
650 | 0 | _aStatistics. | |
650 | 1 | 4 | _aStatistics. |
650 | 2 | 4 | _aStatistical Theory and Methods. |
650 | 2 | 4 | _aStatistics, general. |
650 | 2 | 4 | _aStatistics and Computing/Statistics Programs. |
700 | 1 |
_aStefanski, L. A. _eauthor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9781461448174 |
830 | 0 |
_aSpringer Texts in Statistics, _x1431-875X ; _v120 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-1-4614-4818-1 |
912 | _aZDB-2-SMA | ||
942 |
_2Dewey Decimal Classification _ceBooks |
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999 |
_c44191 _d44191 |