Incomplete data are quite common in biomedical and other types of research, especially in
longitudinal studies. During the last three decades, a vast amount of work has been done in …

Missing data is a major issue in many applied problems, especially in the biomedical
sciences. We review four common approaches for inference in generalized linear models …

Missing data affect nearly every discipline by complicating the statistical analysis of collected
data. But since the 1990s, there have been important developments in the statistical …

This book provides clear instructions to researchers on how to apply Structural Equation
Models (SEMs) for analyzing the inter relationships between observed and latent variables …

This position paper summarizes relevant theory and current practice regarding the analysis
of longitudinal clinical trials intended to support regulatory approval of medicinal products …

In this paper, we carry out robust modeling and influence diagnostics in Birnbaum‐Saunders
(BS) regression models. Specifically, we present some aspects related to BS and log‐BS …

Abstract Cook's [J. Roy. Statist. Soc. Ser. B 48 (1986) 133–169] local influence approach
based on normal curvature is an important diagnostic tool for assessing local influence of …

Nonlinear mixed-effects models are very useful in analyzing repeated-measures data and
have received a lot of attention in the field. In this paper, we propose a method to detect …

Applications of statistical models to describe spatial dependence in geo-referenced data are
widespread across many disciplines including the environmental sciences. Most of these …

In recent years, there has been a growing interest in statistical methods for the analysis of
spatially referenced data. The spatial dependence structure modeling is an indispensable …