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A note on biconic copulas

Fabrizio Durante, Juan Fernández-Sánchez (2011)

Kybernetika

We describe a class of bivariate copulas having a fixed diagonal section. The obtained class contains both the Fréchet upper and lower bounds and it allows to describe non-trivial tail dependence coefficients along both the diagonals of the unit square.

A Note on Computing Extreme Tail Probabilities of the Noncentral t -Distribution with Large Noncentrality Parameter

Viktor Witkovský (2013)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

The noncentral t -distribution is a generalization of the Student’s t -distribution. In this paper we suggest an alternative approach for computing the cumulative distribution function (CDF) of the noncentral t -distribution which is based on a direct numerical integration of a well behaved function. With a double-precision arithmetic, the algorithm provides highly precise and fast evaluation of the extreme tail probabilities of the noncentral t -distribution, even for large values of the noncentrality...

A note on control of the false discovery proportion

Marcin Dudziński, Konrad Furmańczyk (2009)

Applicationes Mathematicae

We consider the problem of simultaneous testing of a finite number of null hypotheses H i , i=1,...,s. Starting from the classical paper of Lehmann (1957), it has become a very popular subject of research. In many applications, particularly in molecular biology (see e.g. Dudoit et al. (2003), Pollard et al. (2005)), the number s, i.e. the number of tested hypotheses, is large and the popular procedures that control the familywise error rate (FWERM) have small power. Therefore, we are concerned with...

A note on interval estimation for the mean of inverse Gaussian distribution.

M. Arefi, G. R. Mohtashami Borzadaran, Y. Vaghei (2008)

SORT

In this paper, we study the interval estimation for the mean from inverse Gaussian distribution. This distribution is a member of the natural exponential families with cubic variance function. Also, we simulate the coverage probabilities for the confidence intervals considered. The results show that the likelihood ratio interval is the best interval and Wald interval has the poorest performance.

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