The present paper deals with the study of the multiple linear regression model for the estimation and prediction of the time series of radon and thoron progeny concentrations in atmosphere. The general purpose of multiple linear regression model is to find the linear relationship between a dependent (or explained) variable and several independent (or predictor) variables. Radon and thoron proge...

Accurate effort prediction of software projects is of concern to portfolio managers, customers, vendors as well as project managers. Ordinary least squares (OLS) regression is widely used to create software prediction models, and it seems to perform just as well or better than most other, non-regression, prediction models. Software data sets may however exhibit certain characteristics that do n...

We study the generation and visualization of residuals for detecting and identifying unseen faults using autoassociative models learned from process data. Least squares and kernel regression models are compared on the basis of their ability to describe the support of the data. Theoretical results show that kernel regression models are more appropriate in this sense. Moreover, experiments on vib...

A previously described mathematical model of Zn absorption as a function of total daily dietary Zn and phytate was fitted to data from studies in which dietary Ca, Fe and protein were also measured. An analysis of regression residuals indicated statistically significant positive relationships between the residuals and Ca, Fe and protein, suggesting that the presence of any of these dietary comp...

In this paper we consider the estimation of the error distribution in a heteroscedastic nonparametric regression model with multivariate covariates. As estimator we consider the empirical distribution function of residuals, which are obtained from multivariate local polynomial fits of the regression and variance functions, respectively. Weak convergence of the empirical residual process to a Ga...

This thesis presents a successful novel approach to the challenging problem of model selection in motion estimation from sequences of images. The concept is based on the brightness change constraint which is discussed formally and concisely such that new light is cast on the properties of local parametric optical flow models. These models give rise to parameter estimation problems with highly c...

This paper characterizes the conditional distribution properties of the finite sample ridge regression estimator and uses that result to evaluate total regression and generalization errors that incorporate the inaccuracies committed at the time of parameter estimation. The paper provides explicit formulas for those errors. Unlike other classical references in this setup, our results take place ...