In this article, a range image sequence tracking approach is proposed, which combines 3-D camera intensity and range observations in an integrated geometric transformation model. Based on 2-D least squares matching, a closed solution for intensity and range observations has been developed. By combining complementary information, an increase in accuracy and reliability can be achieved. The weigh...

A principal component analysis of the 4×4 sire, maternal, management and environmental (co)variance matrices derived from a multi-trait sire model was conducted to describe variability in four economically important carcass traits. Carcass weight (HCWt), P8 fat (P8), eye muscle area (EMA) and intramuscular fat (IMF) collected from 1144 heifers and steers calves from seven sire breeds: Angus, Be...

Probabilistic principal component analysis (PPCA) seeks a low dimensional representation of a data set in the presence of independent spherical Gaussian noise. The maximum likelihood solution for the model is an eigenvalue problem on the sample covariance matrix. In this paper we consider the situation where the data variance is already partially explained by other factors, for example sparse c...

The rebmix package provides R functions for random univariate and multivariate finite mixture model generation, estimation, clustering and classification. The paper is focused on multivariate normal mixture models with unrestricted variance-covariance matrices. The objective is to show how to generate datasets for a known number of components, numbers of observations and component parameters, h...

We present and describe two new speaker adaptation methods which apply principal component analysis to maximum likelihood linear regression (MLLR). If we apply MLLR after transforming the baseline mean vectors by their eigenvectors, the contributions of these eigenvalues to the variance of the estimates for the MLLR matrix are inversely proportional to their corresponding eigenvalues. In the fi...

The restricted maximum likelihood is preferred by many to the full maximum likelihood for estimation with variance component and other random coefficient models, because the variance estimator is unbiased. It is shown that this unbiasedness is accompanied in some balanced designs by an inflation of the mean squared error. An estimator of the cluster-level variance that is uniformly more efficie...

Design of control charts for the mean, the within-run component of variance, and the ratio of between-run to within-run components of variance is outlined. The between-run component of variation is the main source of imprecision for analytes determined by an enzymo- or radioimmunoassay principle; accordingly, explicit control of this component is especially relevant for these types of analytes....

Animal robustness, or environmental sensitivity, may be studied through individual differences in residual variance. These differences appear to be heritable, and there is therefore a need to fit models having breeding values explaining differences in residual variance. The aim of this report is to study whether breeding value estimation for environmental sensitivity (vEBV) can be performed on ...