Measurement error and misclassification of variables frequently occur in epidemiology and
involve variables important to public health. Their presence can impact strongly on results of …

OBJECTIVE: Large health care utilization databases are frequently used in variety of
settings to study the use and outcomes of therapeutics. Their size allows the study of …

The goal of much observational research is to identify risk factors that have a causal effect
on health and social outcomes. However, observational data are subject to biases from …

Background: Exposure measurement error is a central concern in air pollution epidemiology.
Given that studies have been using ambient air pollution predictions as proxy exposure …

Ecological Models and Data in R is the first truly practical introduction to modern statistical
methods for ecology. In step-by-step detail, the book teaches ecology graduate students and …

It's been over a decade since the first edition of Measurement Error in Nonlinear Models
splashed onto the scene, and research in the field has certainly not cooled in the interim. In …

There are many books on various aspects of nonparametric inference such as density
estimation, nonparametric regression, bootstrapping, and wavelets methods. But it is hard …

Over the last 20 years, comprehensive strategies for treating measurement error in complex
models and accounting for the use of extra data to estimate measurement error parameters …

We describe a simulation-based method of inference for parametric measurement error
models in which the measurement error variance is known or at least well estimated. The …

The focus of this book is primarily on the development of pharmacokinetic and
pharmacokinetic-pharmacodynamic models. Models that are reported in the literature are …