A Multivariate Normal Law for Turing’s Formulae

Multivariate Analysis Tests Concerning EquicorrelationMatrices with Grouped Normal Data

This article considers three practical hypotheses involving the equicorrelation matrix for grouped normal data. We obtain statistics and computing formulae for common test procedures such as the score test and the likelihood ratio test. In addition, statistics and computing formulae are obtained for various small sample procedures as proposed in Skovgaard (2001). The properties of the tests for...

Screening for a Multivariate Mixture Normal Distribution

Approximate Closed-form Formulae for Buckling Analysis of Rectangular Tubes under Torsion

The buckling torque may be much less than the yield torque in very thin rectangular tubes under torsion. In this paper, simple closed-form formulae are presented for buckling analysis of long hollow rectangular tubes under torsion. By the presented formulae, one can obtain the critical torque or the critical angle of twist of the tube in terms of its geometrical parameters and material constant...

Turing's unpublished algorithm for normal numbers

In an unpublished manuscript Alan Turing gave a computable construction to show that absolutely normal real numbers between 0 and 1 have Lebesgue measure 1; furthermore, he gave an algorithm for computing instances in this set. We complete his manuscript by giving full proofs and correcting minor errors. While doing this, we recreate Turing’s ideas as accurately as possible. One of his original...

Multivariate truncated moments

1 We derive formulae for the higher order tail moments of the lower truncated multivariate standard normal (MVSN), Student’s t, lognormal and a finite-mixture of multivariate normal distributions (FMVN). For the MVSN we propose a recursive formula for moments of arbitrary order as a generalization of previous research. For the Student’s t-distribution, the recursive formula is an extension of t...