Abstract A common goal of epidemiologic research is to study the association between a certain exposure and a certain outcome, while controlling for important covariates. This is often done by fitting a restricted mean model for the outcome, as in generalized linear models (GLMs) and in...

Summary Mirror data are observations of bilateral variables such as trade from one country to another, reported by both countries. The efficient estimation of a bilateral variable from its mirror data, for example when compiling consistent international trade statistics, requires information...

Optimal designs provide a very efficient way to maximize the amount of information gained in an experiment. For linear models the information is a factor of the covariates, but for non-linear models maximizing the information becomes more difficult because of the relationship of information with...

In survival analysis, the presence of elements not susceptible to the event of interest is very common. These elements lead to what is called a fraction cure, cure rate, or even long-term survivors. In this paper, we propose a unified approach using the negative binomial distribution for...

The paper continues the authors’ work (Freise et al. The adaptive Wynn-algorithm in generalized linear models with univariate response. arXiv:1907.02708, 2019) on the adaptive Wynn algorithm in a nonlinear regression model. In the present paper the asymptotics of adaptive least squares...

We consider the well-known stochastic reserve estimation methods on the basis of generalized linear models, such as the (over-dispersed) Poisson model, the gamma model and the log-normal model. For the likely variability of the claims reserve, bootstrap method is considered. In the bootstrapping...

In public health research, it is common to follow a cohort of subjects over time, observing a vector of health indicators and a set of covariates at each of many visits. An objective of analysis is to characterize the inter-dependencies, in particular, the feedback of one response upon another...

Hierarchical or "multilevel" regression models typically parameterize the mean response conditional on unobserved latent variables or "random" effects and then make simple assumptions regarding their distribution. The interpretation of a regression parameter in such a model is the change in...