Nearexact distributions – comfortable lying closer to exact distributions than common asymptotic distributions
17/10/2017 Tuesday 17th October 2017, 11:00 (Room P3.10, Mathematics Building)
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Carlos A. Coelho, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa
We are all quite familiar with the concept of asymptotic distribution. For some sets of statistics, as it is for example the case with the likelihood ratio test statistics, mainly those used in Multivariate Analysis, some authors developed what are nowadays seen as “standard” methods of building such asymptotic distributions, as it is the case of the seminal paper by Box (1949). However, such asymptotic distributions quite commonly yield approximations which fall short of the precision we need and/or may also exhibit some problems when some parameters in the exact distributions grow large, as it is indeed the case with many asymptotic distributions commonly used in Multivariate Analysis when the number of variables involved grows even just modertely large. The pertinent question is thus the following one: are we willing to pay a bit more in terms of a more elaborate structure for the approximating distribution, anyway keeping it much manageable in terms of allowing for a quite easy computation of pvalues and quantiles? If our answer to the above question is affirmative, then we are ready to enter the amazing world of the nearexact distributions. Nearexact distributions are asymptotic distributions developed under a new concept of approximating distributions. Based on a decomposition (i.e., a factorization or a split in two or more terms) of the characteristic function of the statistic being studied, or of the characteristic function of its logarithm, they are asymptotic distributions which lie much closer to the exact distribution than common asymptotic distributions. If we are able to keep untouched a good part of the original structure of the exact distribution of the random variable or statistic being studied, we may in this way obtain a much better approximation, which not only does not exhibit anymore the problems referred above which occur with some asymptotic distributions, but which on top of this exhibits extremely good performances even for very small sample sizes and large numbers of variables involved, being asymptotic not only for increasing sample sizes but also (opposite to what happens with the common asymptotic distributions) for increasing values of the number of variables involved.
