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A note on order statistics from symmetrically distributed samples

Marek Kałuszka, Andrzej Okolewski (2011)

Applicationes Mathematicae

We present a first moment distribution-free bound on expected values of L-statistics as well as properties of some numerical characteristics of order statistics, in the case when the observations are possibly dependent symmetrically distributed about the common mean. An actuarial interpretation of the presented bound is indicated.

A note on the convolution of inverted-gamma distributions with applications to the Behrens-Fisher distribution.

Francisco Javier Girón, Carmen del Castillo (2001)

RACSAM

La distribución de Behrens-Fisher generalizada se define como convolución de dos distribuciones t de Student y se relaciona con la distribución gamma invertida por medio de un teorema de representación como una mixtura, respecto del parámetro de escala, de distribuciones normales cuando la distribución de mezcla es la convolución de dos distribuciones gamma invertidas. Un resultado importante de este artículo establece que la distribución de Behrens-Fisher con grados de libertad impares es mixtura...

A note on the distribution of angles associated to indefinite integral binary quadratic forms

Dragan Đokić (2019)

Czechoslovak Mathematical Journal

To each indefinite integral binary quadratic form Q , we may associate the geodesic in through the roots of quadratic equation Q ( x , 1 ) . In this paper we study the asymptotic distribution (as discriminant tends to infinity) of the angles between these geodesics and one fixed vertical geodesic which intersects all of them.

A remark on associative copulas

Piotr Mikusiński, Michael D. Taylor (1999)

Commentationes Mathematicae Universitatis Carolinae

A method for producing associative copulas from a binary operation and a convex function on an interval is described.

A second order approximation for the inverse of the distribution function of the sample mean

Jorge M. Arevalillo (2001)

Kybernetika

The classical quantile approximation for the sample mean, based on the central limit theorem, has been proved to fail when the sample size is small and we approach the tail of the distribution. In this paper we will develop a second order approximation formula for the quantile which improves the classical one under heavy tails underlying distributions, and performs very accurately in the upper tail of the distribution even for relatively small samples.

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