A firm concept of time is essential for establishing causality in a clinical setting. Review of
critical incidents and generation of study hypotheses require a robust understanding of the …

The decline in infectious diseases and a rise in chronic diseases, particularly cardiovascular
disease (CVD), underlies the health trajectory of the 20 th century. While much was known …

To increase the application domain (re-use) of LiDAR-based models the random replication
effects in the predictor (s) must be considered. We quantify these effects in a linear predictor …

This article discusses point estimation of the parameters in a linear measurement error
(errors in variables) model when the variances in the measurement errors on both axes vary …

A defining feature of many physiological systems is their synchrony and reciprocal influence.
An important challenge, however, is how to measure such features. This paper presents two …

In the classical errors-in-variables problem, the goal is to estimate a regression curve from
data in which the explanatory variable is measured with error. In this context, nonparametric …

In this paper, we study inference in a heteroscedastic measurement error model with known
error variances. Instead of the normal distribution for the random components, we develop a …

Predicting the value of a variable Y corresponding to a future value of an explanatory
variable X, based on a sample of previously observed independent data pairs (X 1, Y …

Background Clinical networks are increasingly being viewed as an important strategy for
increasing evidence-based practice and improving models of care, but success is variable …

Statistical Testing Strategies in the Health Sciences provides a compendium of statistical
approaches for decision making, ranging from graphical methods and classical procedures …