In the last years there has been a growing interest in the construction space-time covariance functions. However, effective estimation methods for these models are somehow unexplored. In this paper we propose a composite likelihood approach and a weighted variant for the space-time estimation problem. The proposed method can be a valid compromise between the computational burdens, induced by th...

In this article we show that the linear reconciliation problem can be represented by a standard multiple linear regression model. The appropriate criteria for redundancy, determinability and gross error detection are shown to follow in a straightforward manner from the standard theory of linear least squares. The regression approach suggests a natural measure of the redundancy of an observation...

A new method for adaptive autoregressive spectral estimation based on the least-squares criterion with multi-band decomposition of the linear prediction error and analysis of each band through independent variable forgetting factors is presented. The proposed method localizes the forgetting factor adaptation scheme in the frequency domain and in the time domain, in the sense that variations on ...

This paper presents a new feature weighting method for distance-based classifiers. It is based on a generalized least squares minimization of a criterion function to estimate a feature relevance metric. Experiments over both artificial and real data sets illustrate the behaviour of this algorithm when irrelevant attributes and/or features with varying relevance are present. Effectiveness of the...

This paper deals with an on line identification of the values of these entities by a suitable adjusting of the filter gain and incorporating information about the quality of innovation sequence. The result yields a new adaptive Kalman filter. The proposal is mainly based on statistical properties of the innovation sequence of the filter and improves some previous results pointed out by Bellange...

It is a well-known problem that obtaining a correct bandwidth in nonparametric regression is difficult in the presence of correlated errors. There exist a wide variety of methods coping with this problem, but they all critically depend on a tuning procedure which requires accurate information about the correlation structure. Since the errors cannot be observed, the latter is a hard goal to achi...

T HE introduction by Hoerl and Kennard (1970) of a ridge regression estimator to deal with the problem of multicollinearity in regression has been followed by a large number of papers in the statistical literature. In the area of econometrics, though, the method of ridge regression has received little attention. I One of the reasons for the lack of interest in ridge regression on the part of th...

Choosing the performance criterion to be mean squared error matrix, we have compared the least squares and Stein-rule estimators for coefficients in a linear regression model when the disturbances are not necessarily normally distributed. It is shown that none of the two estimators dominates the other, except in the trivial case of merely one regression coefficient where least squares is found ...

In this paper we consider the problem of constructing measurements optimized to distinguish between a collection of possibly non-orthogonal quantum states. We consider a collection of pure states and seek a positive operator-valued measure (POVM) consisting of rank-one operators with measurement vectors closest in squared norm to the given states. We compare our results to previous measurements...

In this paper, a multi-class feature selection scheme based on recursive feature elimination (RFE) is proposed for texture classifications. The feature selection scheme is performed in the context of one-against-all least squares support vector machine classifiers (LSSVM). The margin difference between binary classifiers with and without an associated feature is used to characterize the discrim...