This paper provides parametric and rank-based optimal tests for eigenvectors and eigenvalues of covariance or scatter matrices in elliptical families. The parametric tests extend the Gaussian likelihood ratio tests of Anderson (1963) and their pseudo-Gaussian robustifications by Tyler (1981, 1983) and Davis (1977), with which their Gaussian versions are shown to coincide, asymptotically, under ...

The wave type field equation θ = λθ, where θ is a coframe field on a space-time, was recently proposed to describe the gravity field. This equation has a unique static, spherical-symmetric, asymptotically-flat solution, which leads to the viable Yilmaz-Rosen metric. We show that the wave type field equation is satisfied by the pseudo-conformal frame if the conformal factor is determined by a sc...

Classical techniques for analysing multivariate extremes can often be framed in terms of the point process representation of de Haan (1985). Amongst other things, this representation provides a characterisation of the limiting distribution of the normalised componentwise maxima of independent and identically distributed unit Fréchet variables, i.e. the class of multivariate extreme value distri...

We present an efficient generalization of the sparse pseudo-input Gaussian process (SPGP) model developed by Snelson and Ghahramani [1], applying it to binary classification problems. By taking advantage of the SPGP prior covariance structure, we derive a numerically stable algorithm with O(NM) training complexity—asymptotically the same as related sparse methods such as the informative vector ...

Codewords in finite covers of a Tanner graph G are characterized. Since iterative, locally operating decoding algorithms cannot distinguish the underlying graph G from any covering graph, these codewords, dubbed pseudo-codewords are directly responible for sub-optimal behavior of iterative decoding algorithms. We give a simple characterization of pseudocodewords from finite covers and show that...

We present an efficient generalization of the sparse pseudo-input Gaussian process (SPGP) model developed by Snelson and Ghahramani [1], applying it to binary classification problems. By taking advantage of the SPGP prior covariance structure, we derive a numerically stable algorithm with O(NM) training complexity—asymptotically the same as related sparse methods such as the informative vector ...

Statistical estimation and inference for marginal hazard models with varying coefficients for multivariate failure time data are important subjects in survival analysis. A local pseudo-partial likelihood procedure is proposed for estimating the unknown coefficient functions. A weighted average estimator is also proposed in an attempt to improve the efficiency of the estimator. The consistency a...

In this paper we introduce new modified implicit and explicit algorithms and prove strong convergence of the two algorithms to a common fixed point of a family of uniformly asymptotically regular asymptotically nonexpansive mappings in a real reflexive Banach space  with a uniformly G$hat{a}$teaux differentiable norm. Our result is applicable in $L_{p}(ell_{p})$ spaces, $1 < p

We study in this paper quasiperiodic maximal surfaces pseudo-hyperbolic spaces and show that they are characterised by a curvature condition, Gromov hyperbolicity or conformal hyperbolicity. the limit curves of these Einstein Universe admits canonical quasisymmetric parametrisation, while conversely every curve bounds surface such way parametrisation is continuous extension uniformisation; we g...