AbstractThe density of the Langevin (or Fisher-Von Mises) distribution is proportional to exp κμ′x, where x and the modal vector μ are unit vectors in Rq. κ (≥0) is called the concentration parameter. The distribution of statistics for testing hypotheses about the modal vectors of m distributions simplify greatly as the concentration parameters tend to infinity. The non-null distributions are obtained for statistics appropriate when κ1,…,κm are known but tend to infinity, and are unknown but equal to κ which tends to infinity. The three null hypotheses are H01:μ = μ0(m=1), H02:μ1 = … =μm, H03:μi ϵ V, i=1,…,m In each case a sequence of alternatives is taken tending to the null hypothesis
Location, spread, skewness and tailweight are studied for unimodal distributions by means of mode-ba...
We consider asymptotic inference for the concentration of directional data. More precisely, wepropos...
We describe a Langevin diffusion with a target stationary density with respect to Lebesgue measure, ...
AbstractThe density of the Langevin (or Fisher-Von Mises) distribution is proportional to exp κμ′x, ...
AbstractThis paper concerns the matrix Langevin distributions, exponential-type distributions define...
One-sample and multi-sample tests on the concentration parameter of Fisher-von Mises-Langevin distri...
AbstractIn this paper we study the asymptotic behaviors of the likelihood ratio criterion (TL(s)), W...
peer reviewedOne-sample and multi-sample tests on the concentration parameter of Fisher-von Mises-La...
A multi-sample test for equality of mean directions is developed for populations having Langevin-von...
We consider concepts and models that are useful for measuring how strongly the distribution of a pos...
We consider concepts and models that are useful for measuring how strongly the distribution of a pos...
<p>The density is plotted for μ = 0 and different values of the concentration parameter κ.</p
International audienceIn directional statistics, the von Mises distribution is a key element in the ...
Bobkov SG, Götze F. Concentration of empirical distribution functions with applications to non-i.i.d...
In high-dimensional directional statistics one of the most basic probability distributions is the vo...
Location, spread, skewness and tailweight are studied for unimodal distributions by means of mode-ba...
We consider asymptotic inference for the concentration of directional data. More precisely, wepropos...
We describe a Langevin diffusion with a target stationary density with respect to Lebesgue measure, ...
AbstractThe density of the Langevin (or Fisher-Von Mises) distribution is proportional to exp κμ′x, ...
AbstractThis paper concerns the matrix Langevin distributions, exponential-type distributions define...
One-sample and multi-sample tests on the concentration parameter of Fisher-von Mises-Langevin distri...
AbstractIn this paper we study the asymptotic behaviors of the likelihood ratio criterion (TL(s)), W...
peer reviewedOne-sample and multi-sample tests on the concentration parameter of Fisher-von Mises-La...
A multi-sample test for equality of mean directions is developed for populations having Langevin-von...
We consider concepts and models that are useful for measuring how strongly the distribution of a pos...
We consider concepts and models that are useful for measuring how strongly the distribution of a pos...
<p>The density is plotted for μ = 0 and different values of the concentration parameter κ.</p
International audienceIn directional statistics, the von Mises distribution is a key element in the ...
Bobkov SG, Götze F. Concentration of empirical distribution functions with applications to non-i.i.d...
In high-dimensional directional statistics one of the most basic probability distributions is the vo...
Location, spread, skewness and tailweight are studied for unimodal distributions by means of mode-ba...
We consider asymptotic inference for the concentration of directional data. More precisely, wepropos...
We describe a Langevin diffusion with a target stationary density with respect to Lebesgue measure, ...