Spatial data have become increasingly common in epidemiology and public health research thanks to advances in GIS (Geographic Information Systems) technology. In health research, for example, it is common for epidemiologists to incorporate geographically indexed data into their studies. In practice, however, the spatially defined covariates are often measured with error. Naive estimators of reg...

Corrected score (Nakamura, 1990; Stefanski, 1989) is an important consistent functional modeling method for covariate measurement error in nonlinear regression. Although its pathological behaviors are known to exacerbate with increasing error contamination, neither their nature nor severity is well understood. In this article, we conduct a detailed investigation with the log-linear model for co...

The difference in restricted mean survival times between two groups is a clinically relevant summary measure. With observational data, there may be imbalances in confounding variables between the two groups. One approach to account for such imbalances is estimating a covariate-adjusted restricted mean difference by modeling the covariate-adjusted survival distribution and then marginalizing ove...

We test the presence of a change of slope in a logistic regression model with covariate measured with errors. Under the null hypothesis of no change-point, estimation of a single intercept and slope can be carried out straightforwardly by various conditional score based methods. If the alternative hypothesis holds and indeed there exists a change-point, estimation becomes more challenging, neve...

A common assumption in supervised learning is that the training and test input points follow the same probability distribution. However, this assumption is not fulfilled, e.g., in interpolation, extrapolation, active learning, or classification with imbalanced data. The violation of this assumption—known as the covariate shift— causes a heavy bias in standard generalization error estimation sch...

Active learning approaches used in practice are generally optimistic about their certainty with respect to data shift between labeled and unlabeled data. They assume that unknown datapoint labels follow the inductive biases of the active learner. As a result, the most useful datapoint labels— ones that refute current inductive biases—are rarely solicited. We propose an adversarial approach to a...

We consider the analysis of clustered data using linear mixed effects models and generalized estimating equations, where covariates can be decomposed into betweenand within-cluster components. Under the false assumption of equal betweenand within-cluster covariate effects, we simultaneously study the asymptotic behavior of the estimators for regression coefficients, intra-cluster correlation an...

Many areas of applied statistics have become aware of the problem of measurement error-prone variables and their appropriate analysis. Simply ignoring the error in the analysis usually leads to biased estimates, like e.g. in the regression with error-prone covariates. While this problem has been discussed at length for parametric regression, only few methods exist to handle nonparametric regres...

BACKGOUND The term 'joint modelling' is used in the statistical literature to refer to methods for simultaneously analysing longitudinal measurement outcomes, also called repeated measurement data, and time-to-event outcomes, also called survival data. A typical example from nephrology is a study in which the data from each participant consist of repeated estimated glomerular filtration rate (e...