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 …

Abstract The US Census Bureau will deliberately corrupt data sets derived from the 2020 US
Census in an effort to maintain privacy, suggesting a painful trade-off between the privacy of …

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 …

Today, the need to increase efficiency and performance of dynamical systems leads to
innovative control solutions that rely on accurate representations of the underlying system …

Widely used convolution and deconvolution techniques traditionally rely on independence
assumptions, often criticized as being strong. We observe that the convolution theorem …

In this paper, we consider the estimation problem of a correlation coefficient between
unobserved variables of interest. These unobservable variables are distorted in a …

Researchers in the medical, health, and social sciences routinely encounter ordinal
variables such as self‐reports of health or happiness. When modelling ordinal outcome …

Measurement errors are often correlated, as in surveys where respondent's biases or
tendencies to err affect multiple reported variables. We extend Schennach (2007) to identify …

The paper presents the method of moments estimation for generalized linear measurement
error models using the instrumental variable approach. The measurement error has a …