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 ...

Background: A new algorithm for precise characterisation of rotationally symmetric aspheric surfaces by the conic section and polynomial according to the ISO 10110 standard is described. Methods: The algorithm uses only the iterative linear least squares. It uses fitting the surface form in a combination with terms containing its spatial derivatives that represent infinitesimal transformations ...

This paper considers two recently proposed semiparametric estimators for distribution-free binary response models under a conditional median restriction. It shows that these estimators can be implemented in Stata using nl through simple modifications to the nonlinear least squares probit criterion function. We then introduce dfbr, a new Stata command which implements these estimators, and provi...

− The spectrophotometric analysis of oil mixtures, containing olive oil, is the subject of this paper. Its objective is to propose and evaluate a new methodology for determination of a selected component of such a mixture. According to this methodology, the space of concentration values to be estimated is partitioned into subspaces and the calibration is performed separately for each subspace b...

B-splines can be used to approximate data in a variety of metrology applications. In general, the approximation is carried out so as to minimize a criterion deened in terms of the least squares norm. However, often this choice of norm is inappropriate , and a measure such as thè 1 or minimax norm should be chosen. In this tutorial paper, we introduce the topic of B-splines, and we discuss the a...

This paper establishes that when using a least squares criterion to estimate an output error type model structure, then the measurement noise induced variability of the frequency response estimate depends on the estimated (and hence also on the true) pole positions. This dependence on pole position is perhaps counter to prevailing wisdom that for any ‘shift invariant’ model structure, the varia...

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...

For the problem of estimation of Money demand model of Pakistan, money supply (M1) shows heteroscedasticity of the unknown form. For estimation of such model we compare two adaptive estimators with ordinary least squares estimator and show the attractive performance of the adaptive estimators, namely, nonparametric kernel estimator and nearest neighbour regression estimator. These comparisons a...