Abstmct-Recently there seems to have been a resurgence of interest in recursive parameter-bounding algorithms. These algorithms are applicable when the noise is bounded and the bound is known to the user. One of the advantages of such algorithms is that 100% confidence regions (which are optimal in some sense) for the parameter estimates can be obtained at every time instant, rather than asympt...

This paper shows how asymptotically valid inference in regression models based on the weighted least squares (WLS) estimator can be obtained even when the model for reweighting the data is misspecified. Like the ordinary least squares estimator, the WLS estimator can be accompanied by heterokedasticty-consistent (HC) standard errors without knowledge of the functional form of conditional hetero...

We present a new hierarchic kernel based modeling technique for modeling evenly distributed multidimensional datasets that does not rely on input space sparsification. The presented method reorganizes the typical single-layer kernel based model in a hierarchical structure, such that the weights of a kernel model over each dimension are modeled over the adjacent dimension. We show that the impos...