Percentage error (relative to the observed value) is often felt to be more meaningful than the absolute error in isolation. The mean absolute percentage error (MAPE) is widely used in forecasting as a basis of comparison, and regression models can be fitted which minimize this criterion. Unfortunately, no formula exists for the coefficients, and models for a given data set may not be unique. We...

A large study conducted at two southeastern US hospitals from October 2007 through October 2008 sought to identify predictive variables for successful intravenous catheter (IV) insertion, a crucial procedure that is potentially difficult and time consuming in young children. The data was collected on a sample of 592 children that received a total of 1,195 attempts to start peripheral IV cathete...

The goal of phylogenetic inference is the reconstruction of the evolutionary history of various biological entities (taxa) such as genes, proteins, viruses or species. Phylogenetic inference is of major importance in computational biology and has numerous applications ranging from the study of biodiversity to sequence analysis. Given a matrix of pairwise distances between taxa, the minimum evol...

A new forward regression model identification algorithm is introduced. The derived model parameters, in each forward regression step, are initially estimated via orthogonal least squares (OLS) (using the modified Gram-Schmidt procedure), followed by being tuned with a new gradient descent learning algorithm based on the basis pursuit that minimizes the norm of the parameter estimate vector. The...

In this brief, a robust and sparse recursive adaptive filtering algorithm, called convex regularized recursive maximum correntropy (CR-RMC), is derived by adding a general convex regularization penalty term to the maximum correntropy criterion (MCC). An approximate expression for automatically selecting the regularization parameter is also introduced. Simulation results show that the CR-RMC can...

Acknowledgements: This work is part of the EU project Growth " EUPIGCLASS " (GRD1-1999-10914) partly funded through the 5 th Framework Programme , the Growth Generic Activity Measurement and Testing, and also partly through the Technological Development Programme from OFIVAL. Many thanks are due to Rainer Nagel and Juan Alvarez from the Pig and Poultry Office of the General Direction of Agricul...

This paper considers the problem of estimating the parameters of chirp signals with randomly time-varying amplitude. Two methods for solving this problem are presented. First, a nonlinear least-squares approach (NLS) is proposed. It is shown that by minimizing the NLS criterion with respect to all samples of the time-varying amplitude, the problem reduces to a two-dimensional maximization probl...

Modified (r, k) class ridge regression (MCRR) which includes unbiased ridge regression (URR), (r, k) class, principal components regression (PCR) and the ordinary least squares (OLS) estimators is proposed in regression analysis, to overcome the problem of multicollinearity. In this paper, we derive the necessary and sufficient conditions for the superiority of the MCRR estimator over each of t...

The quality of short-term electricity load forecasting is crucial to the operation and trading activities of market participants in an electricity market. In this paper, it is shown that a multiple equation time-series model, which is estimated by repeated application of ordinary least squares, has the potential to match or even outperform more complex nonlinear and nonparametric forecasting mo...