Classical least squares regression consists of minimizing the sum of the squared residuals. Many authors have produced more robust versions of this estimator by replacing the square by something else, such as the absolute value. In this article a different approach is introduced in which the sum is replaced by the median of the squared residuals. The resulting estimator can resist the effect of...

The complexation reactions of some transition metal ions and 2-aminopyridine (2-ampy) were studied potentiometrically in aqueous solution at µ=0.1 M and 25 °C. The overall stability constants log β’s of all species are obtained by computer refinement of pH-volume data with using the BEST computer program. Several models were tested and the best one accepted according to the least sum of squ...

In this paper, we present a new algorithm for solving the block matching problem which is independent of image content and is faster than other full-search methods. The method employs a novel data structure called the Windowed-Sum-Squared-Table, and uses the fast Fourier transform (FFT) in its computation of the sum squared difference (SSD) metric. Use of the SSD metric allows for higher peak s...

Several splitting criteria for binary classification trees are shown to be written as weighted sums of two values of divergence measures. This weighted sum approach is then used to form two families of splitting criteria. One of them contains the chi-squared and entropy criterion, the other contains the mean posterior improvement criterion. Both family members are shown to have the property of ...

Introduction When estimating the parameters of the diffusion tensor it is common to choose those that minimise the sum of squared differences between the observed data and the predictions made by the model. This is equivalent to a Maximum Likelihood (ML) estimate given an assumption of normal distributed errors. In reality the errors have a Rician distribution, but when the SNR is sufficiently ...