429 research outputs found
Student's -test for scale mixture errors
Generalized t-tests are constructed under weaker than normal conditions. In
the first part of this paper we assume only the symmetry (around zero) of the
error distribution (i). In the second part we assume that the error
distribution is a Gaussian scale mixture (ii). The optimal (smallest) critical
values can be computed from generalizations of Student's cumulative
distribution function (cdf), . The cdf's of the generalized -test
statistics are denoted by (i) and (ii) , resp. As the
sample size we get the counterparts of the standard normal cdf
: (i) , and (ii)
. Explicit formulae are
given for the underlying new cdf's. For example iff . Thus the classical 95% confidence interval for the unknown expected
value of Gaussian distributions covers the center of symmetry with at least 95%
probability for Gaussian scale mixture distributions. On the other hand, the
90% quantile of is .Comment: Published at http://dx.doi.org/10.1214/074921706000000365 in the IMS
Lecture Notes--Monograph Series
(http://www.imstat.org/publications/lecnotes.htm) by the Institute of
Mathematical Statistics (http://www.imstat.org
Egy vita margĂłjĂĄra: a KommunistĂĄk MagyarorszĂĄgi PĂĄrtjĂĄnak vezetĆi elleni perek MoszkvĂĄban - Kun BĂ©la, LukĂĄcs György Ă©s Nagy Imre pĂ©ldĂĄjĂĄn
Az EgyesĂŒlt Ăllamok Ă©s a NĂ©pszövetsĂ©g â elkerĂŒlhetĆ lett volna a II. vilĂĄghĂĄborĂș?
DISCO analysis: A nonparametric extension of analysis of variance
In classical analysis of variance, dispersion is measured by considering
squared distances of sample elements from the sample mean. We consider a
measure of dispersion for univariate or multivariate response based on all
pairwise distances between-sample elements, and derive an analogous distance
components (DISCO) decomposition for powers of distance in . The ANOVA F
statistic is obtained when the index (exponent) is 2. For each index in
, this decomposition determines a nonparametric test for the
multi-sample hypothesis of equal distributions that is statistically consistent
against general alternatives.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS245 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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