This paper considers the branching process described in [8] and generated by an offspring distribution F with mean m and variance oe 2 , with binomial migration, . We give a strong consistent estimato...
This paper considers a branching process generated by an offspring distribution F with mean m and variance oe 2 , with binomial migration. The paper deals with the asymptotic behaviour of the migratio...
. We describe and study in this paper a two step estimation scheme for density estimation from i.i.d. observations. Each step is based on the Gibbs aggregation rule and computes an adaptive histogram ...
An data-dependent method for choosing the amount of smoothing when estimating quadratic functionals is given. It is shown that the method is asymptotically optimal adaptive up to a constant. The metho...
In this paper we address the problem of the stability of the stochastic approximation procedure. The stability of such algorithms is known to rely heavily on the growth of the mean eld at the boundary...
Lepski's method is a method for choosing a \best" estimator (in an appropriate sense) among a family of those, under suitable restrictions on this family. The subject of this paper is to give a nonasy...
Our purpose in this paper is to build some very simple piecewise constant estimators for interval censoring cases I and II and to derive some of their properties, in particular some nonasymptotic risk...