This paper presents a new direct method for estimating the average center of rotation (CoR). An existing least-squares (LS) solution has been shown by previous works to have reduced accuracy for data with small range of motion (RoM). Alternative methods proposed to improve the CoR estimation use iterative algorithms. However, in this paper we show that with a carefully chosen normalization sche...

The main object of investigation in this paper is a very general regression model optional setting - when an observed process semimartingale depending on unknown parameter. It well-known that statistical data may present information flow/filtration without usual conditions. estimation problem achieved by means structural least squares (LS) estimates and their sequential versions. results the ar...

Motivated by the advances in computer technology and the fact that the batch/block least-squares (LS) produces more accurate parameter estimates than its recursive counterparts, several important issues associated with the block LS have been re-examined in the framework of on-line identification of systems with abrupt/gradual change parameters in this paper. It is no surprise that the standard ...

A new data-aided frequency estimator for frequencyselective fading channels is introduced. The proposed estimator is developed based on a least squares (LS) error criterion and can estimate frequency offsets without the need for channel information. Statistical analysis indicates that the resulting estimate is unbiased and tends to approach the Cramér–Rao lower bound (CRLB). Simulation shows th...

In this paper, we propose some improvements for the implementations of least squares support vector machine classifiers (LS-SVM). An improved conjugate gradient scheme is proposed for solving the optimization problems in LS-SVM, and an improved SMO algorithm is put forward for the general unconstrained quadratic programming problems which is the case of LS-SVM without the bias term. Numerical e...

Least-Squares Support Vector Machines (LS-SVMs) have been successfully applied in many classification and regression tasks. Their main drawback is the lack of sparseness of the final models. Thus, a procedure to sparsify LS-SVMs is a frequent desideratum. In this paper, we adapt to the LS-SVM case a recent work for sparsifying classical SVM classifiers, which is based on an iterative approximat...

Using a recently proposed communication optimal variant of TSQR, weak scalability of the least squares solver (LS) with multiple right hand sides is studied. The communication for TSQR based LS solver for multiple right hand sides remains optimal in the sense that no additional messages are necessary compared to TSQR. However, LS has additional communication volume and flops compared to that fo...

The Volterra delay integral equations have numerous applications in various branches of science, including biology, ecology, physics and modeling of engineering and natural sciences. In many cases, it is difficult to obtain analytical solutions of these equations. So, numerical methods as an efficient approximation method for solving Volterra delay integral equations are of interest to many res...

Coefficient estimation and variable selection in multiple linear regression is routinely done in the (penalized) least squares (LS) framework. The concept of model selection oracle introduced by Fan and Li [J. Amer. Statist. Assoc. 96 (2001) 1348–1360] characterizes the optimal behavior of a model selection procedure. However, the least-squares oracle theory breaks down if the error variance is...

This paper presents a novel algorithm for least squares (LS) estimation of both stationary and nonstationary signals which arise from Volterra models. The algorithm concerns the recursive implementations of the method of LS which usually have a weighting factor in the cost function. This weighting factor enables nonstationary signal models to be tracked. In particular, the behavior of the weigh...