In 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. Author resource page: http:
//www.isib.cnr.it/~brazzale/AA/
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