000			02995cam a2200397 a 4500
001			93005212 //r94
003			DLC
005			20240430143950.0
008			050915s1993 caua b 000 0 eng
010			_a 93005212 //r94
020			_a080395381X : _c14.95
040			_aDLC _cDLC _dDLC
050	0	0	_aHA31.2 _b.M66 1993
069			_a03407590
090			_aHA 31.2 .M66 1993
090			_aHA 31.2 .M66 1993
100	1		_aMooney, Christopher Z. _974103
245	1	0	_aBootstrapping : _ba nonparametric approach to statistical inference / _cChristopher Z. Mooney, Robert D. Duval.
260			_aNewbury Park, Calif. : _bSage Publications, _cc1993.
300			_avi, 73 p. : _bill. ; _c22 cm.
440		0	_aSage university papers series. _pQuantitative applications in the social sciences ; _vno. 07-095 _974104
504			_aIncludes bibliographical references (p. 68-72).
505	0		_aTraditional Parametric Statistical Inference -- Bootstrap Statistical Inference -- Bootstrapping a Regression Model -- Theoretical Justification -- The Jackknife -- Monte Carlo Evaluation of the Bootstrap -- Statistical Inference Using the Bootstrap -- Bias Estimation -- Bootstrap Confidence Intervals -- Applications of Bootstrap Confidence Intervals -- Confidence Intervals for Statistics With Unknown Sampling Distributions -- The Sample Mean From a Small Sample -- The Difference Between Two Sample Medians -- Inference When Traditional Distributional Assumptions Are Violated -- OLS Regression With a Nonnormal Error Structure -- Future Work -- Limitations of the Bootstrap -- Bootstrapping With Statistical Software Packages.
520		0	_a"This book is. . . clear and well-written. . . anyone with any interest in the basis of quantitative analysis simply must read this book. . . . well-written, with a wealth of explanation. . ." --Dougal Hutchison in Educational Research Using real data examples, this volume shows how to apply bootstrapping when the underlying sampling distribution of a statistic cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, it discusses the advantages and limitations of four bootstrap confidence interval methods--normal approximation, percentile, bias-corrected percentile, and percentile-t. The book concludes with a convenient summary of how to apply this computer-intensive methodology using various available software packages.
650		0	_aSocial sciences _xStatistical methods. _974105
650		0	_aBootstrap (Statistics) _974106
650		0	_aInference. _974107
700	1		_aDuval, Robert. _916650
852			_9p14.95 _y09-14-2002
907			_a13471 _b08-06-10 _c08-06-10
942			_cBOOK _00
998			_aaudmc _b09-14-02 _cm _da _e- _feng _gcau _h0
935			_aPO10329-GEN3
945			_g0 _i631101 _j0 _laudmc _nCopy Type:01 - Books _o- _p54.94 _q- _r- _s- _t1 _u0 _v0 _w0 _x0 _yi10190296 _z08-06-10
999			_c13471 _d13471