S U M M A R Y With the aim of treating the statistics of palaeomagnetic directions and intensities jointly and consistently, we represent the mean and the variance of palaeomagnetic vectors, at a particular site and of a particular polarity, by a probability density function in a Cartesian three-space of orthogonal magnetic-field components consisting of a single (unimodal) non-zero mean, spher...

This paper describes the R package cold for analysis of count longitudinal data. In this marginal and random effects models are considered. both cases estimation is via maximization exact likelihood serial dependence among observations assumed to be Markovian type referred as integer-valued autoregressive order one process. For adaptive Gaussian quadrature Monte Carlo methods used compute integ...

The linear vector-valued channel + with and denoting additive white Gaussian noise and independent random matrices, respectively, is analyzed in the asymptotic regime as the dimensions of the matrices and vectors involved become large. The asymptotic eigenvalue distribution of the channel’s covariance matrix is given in terms of an implicit equation for its Stieltjes transform as well as an exp...

This paper deals with efficient algorithms for simulating performance measures of Gaussian random vectors. Recently, we developed a simulation algorithm which consists of doing importance sampling by shifting the mean of the Gaussian random vector. Further variance reduction is obtained by stratification along a key direction. A central ingredient of this method is to compute the optimal shift ...

This note presents a unified analysis of the recovery of simple objects from random linear measurements. When the linear functionals are Gaussian, we show that an s-sparse vector in R can be efficiently recovered from 2s log n measurements with high probability and a rank r, n×n matrix can be efficiently recovered from r(6n − 5r) measurements with high probability. For sparse vectors, this is w...

A model for simulation of non-stationary, non-Gaussian processes based on non-linear translation of Gaussian random vectors is presented. This method is a generalization of traditional translation processes that includes the capability of simulating samples with spatially or temporally varying marginal probability density functions. A formal development of the properties of the resulting proces...

Dimensionality reduction is in demand to reduce the complexity of solving largescale problems with data lying in latent low-dimensional structures in machine learning and computer version. Motivated by such need, in this work we study the Restricted Isometry Property (RIP) of Gaussian random projections for low-dimensional subspaces in R , and rigorously prove that the projection Frobenius norm...

This paper presents a new classification method for single subject’s motion. We employ R transform descriptor and Linear-chain Conditional Random Fields for representation and classification. What it solves is that global features are described and adjacent states are independent. We extract binary silhouettes from a video sequence and segment them into groups by cycle after building the backgr...

This paper derives central limit and bootstrap theorems for probabilities that sums of centered high-dimensional random vectors hit hyperrectangles and sparsely convex sets. Specifically, we derive Gaussian and bootstrap approximations for probabilities P(n−1/2 ∑n i=1 Xi ∈ A) where X1, . . . , Xn are independent random vectors in R and A is a hyperrectangle, or, more generally, a sparsely conve...