The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
En este trabajo definimos una medida de centralización multidimensional para vectores aleatorios como el valor del parámetro para el que se alcanza el mínimo de las integrales de ciertas funciones. Estudiamos su relación con otras medidas de centralización multidimensionales conocidas. Finalizamos demostrando la Ley Fuerte de los Grandes Números, tanto para la medida de centralización definida como para la de dispersión asociada.
In a probability space (Ω,σ,P), for α ⊂ σ a sub-σ field, in general the best approximation in L by elements of L(α) has not a unique solution. For the election between these, we prove the convergence P-almost surely of the conditional r-means, when r → ∞, to one solution, which we call conditional mid-range. This is characterized for each ω ∈ Ω by the mid-range, of one regular conditional distribution Q(ω, ·).
Download Results (CSV)