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...

type="main" xml:id="sjos12028-abs-0001" <title type="main">ABSTRACT</title>This paper considers inference for both spatial lattice data with possibly irregularly shaped sampling region and non-lattice data, by extending the recently proposed self-normalization (SN) approach from stationary time series to the spatial...