This article reviews recent significant progress made in developing estimation and inference
methods for nonlinear models in the presence of mismeasured data that may or may not …

Although standard mixed effects models are useful in a range of studies, other approaches
must often be used in correlation with them when studying complex or incomplete data …

Deconvolution problems occur in many fields of nonparametric statistics, for example,
density estimation based on contaminated data, nonparametric regression with errors-in …

The issues caused by measurement errors have been recognized for almost 90 years, and
research in this area has flourished since the 1980s. We review some of the classical …

Semiparametric regression is a fusion between parametric regression and nonparametric
regression that integrates low-rank penalized splines, mixed model and hierarchical …

Robust statistical methods were developed to supplement the classical procedures when
the data violate classical assumptions. They are ideally suited to applied research across a …

This chapter overviews the recent progress towards the identification and the estimation of
models in which some of the variables are either imperfectly measured or even entirely …

Economic models often depend on quantities that are unobservable, either for privacy
reasons or because they are difficult to measure. Examples of such variables include human …

SIMEX methods are attractive for solving curve estimation problems in errors-in-variables
regression, using parametric or semiparametric techniques. However, nonparametric …

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 …