• The R package mecor accommodates measurement error correction in linear regression models with a continuous outcome. implements by means of calibration, maximum likelihood estimation and method moments. methods for four different validation data structures: internal, replicates, calibration external data. When no additional is available, framework conducting sensitivity analyses provided. Mea...

This paper studies the impact of channel coherence and the role of channel state information (CSI) on the probability of error in wideband multipath fading channels. Inspired by recent ultra wideband channel measurement campaigns, we propose a sparse channel model for time and frequency selective fading in which the number of resolvable channel paths grow sub-linearly with signal space dimensio...

The nature of social science research means that many variables we are interested in are also difficult to measure, making measurement error a particular concern. In simple correlation and regression, unreliable measurement causes relationships to be under-estimated increasing the risk of Type II errors. In the case of multiple regression or partial correlation, effect sizes of other variables ...

Conclusions In the proportional hazards model the effect of a covariate is assumed to be time-invariant. In this paper a graphical method based on a linear regression model (LRM) is used to test whether this assumption is realistic. The variation in the effect of a covariate is plotted against time. The slope of this plot indicates the nature of the influence of a covariate over time. A covaria...

We consider a non-parametric model for estimating the effect of a binary treatment on an outcome variable while adjusting for an observed covariate. A naive procedure consists in performing two separate non-parametric regression of the response on the covariate: one with the treated individuals and the other with the untreated. The treatment effect is then obtained by taking the difference betw...

Covariate adjustment is a commonly used method for total causal effect estimation. In recent years, graphical criteria have been developed to identify all valid sets, that is, covariate sets can be this purpose. Different typically provide estimates of varying accuracies. Restricting ourselves linear models, we introduce criterion compare the asymptotic variances provided by certain sets. We em...

Univariate or multivariate ordinal responses are often assumed to arise from a latent continuous parametric distribution, with covariate effects which enter linearly. We introduce a Bayesian nonparametric modeling approach for univariate and multivariate ordinal regression, which is based on mixture modeling for the joint distribution of latent responses and covariates. The modeling framework e...

An Appendix with proofs and tuning details has been added here. Abstract Classical penalized likelihood regression problems deal with the case that the independent variables data are known exactly. In practice, however, it is common to observe data with incomplete covariate information. We are concerned with a fundamentally important case where some of the observations do not represent the exac...

Feed forward Neural Networks FNNs and Support Vector Machines SVMs are two ma chine learning frameworks developed from very dif ferent starting points of view In this work a new learning model for FNNs is proposed such that in the linearly separable case tends to obtain the same solution that SVMs The key idea of the model is a weighting of the sum of squares error function which is inspired in...