000 02393nam a22003618i 4500
001 CR9780511611131
003 UkCbUP
005 20200124160220.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 090910s2007||||enk o ||1 0|eng|d
020 _a9780511611131 (ebook)
020 _z9780521847032 (hardback)
040 _aUkCbUP
_beng
_erda
_cUkCbUP
050 4 _aQA277
_b.B73 2007
082 0 4 _a519.6
_222
100 1 _aBrazzale, A. R.
_q(Alessandra R.),
_eauthor.
245 1 0 _aApplied asymptotics :
_bcase studies in small-sample statistics /
_cA.R. Brazzale, A.C. Davison, N. Reid.
264 1 _aCambridge :
_bCambridge University Press,
_c2007.
300 _a1 online resource (viii, 236 pages) :
_bdigital, PDF file(s).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aCambridge series on statistical and probabilistic mathematics ;
_v23
500 _aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
520 _aIn fields such as biology, medical sciences, sociology, and economics researchers often face the situation where the number of available observations, or the amount of available information, is sufficiently small that approximations based on the normal distribution may be unreliable. Theoretical work over the last quarter-century has led to new likelihood-based methods that lead to very accurate approximations in finite samples, but this work has had limited impact on statistical practice. This book illustrates by means of realistic examples and case studies how to use the new theory, and investigates how and when it makes a difference to the resulting inference. The treatment is oriented towards practice and comes with code in the R language (available from the web) which enables the methods to be applied in a range of situations of interest to practitioners. The analysis includes some comparisons of higher order likelihood inference with bootstrap or Bayesian methods.
650 0 _aStatistical hypothesis testing
_xAsymptotic theory.
700 1 _aDavison, A. C.
_q(Anthony Christopher),
_eauthor.
700 1 _aReid, N.,
_eauthor.
776 0 8 _iPrint version:
_z9780521847032
830 0 _aCambridge series on statistical and probabilistic mathematics ;
_v23.
856 4 0 _uhttps://doi.org/10.1017/CBO9780511611131
999 _c516608
_d516606