In this paper we consider some iterative estimation algorithms, which are valid to analyse the variance of data, which may be either non-grouped or grouped with different classification intervals. This situation appears, for instance, when data is collected from different sources and the...

We present in this paper iterative estimation procedures, using conditional expectations, to fit linear models when the distributions of the errors are general and the dependent data stem from a finite number of sources, either grouped or non-grouped with different classification criteria. We...

type="main" xml:id="sjos12032-abs-0001" <title type="main">ABSTRACT</title>It has been shown in literature that the Lasso estimator, or ℓ₁-penalized least squares estimator, enjoys good oracle properties. This paper examines which special properties of the ℓ₁-penalty allow for sharp oracle results, and then extends...