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 …

We study estimation and testing in the Poisson regression model with noisy high
dimensional covariates, which has wide applications in analyzing noisy big data. Correcting …

Large-scale datasets with count outcome variables are widely present in various
applications, and the Poisson regression model is among the most popular models for …

In practice, measurement error data is frequently encountered and needs to be handled
appropriately. As a result of additional bias induced by measurement error, many existing …

We study the well known difficult problem of prediction in measurement error models. By
targeting directly at the prediction interval instead of the point prediction, we construct a …

Poisson regression is a statistical method specifically designed for analyzing count data.
Considering the case where the functional and vector-valued covariates exhibit a linear …

High-dimensional measurement error data are becoming more prevalent across various
fields. Research on measurement error regression models has gained momentum due to …

This thesis proposes a novel statistical inference framework for high-dimensional
generalized linear models (GLMs). The first project focuses on labeling patients in electronic …

We generalize the naive estimator of a Poisson regression model with a measurement error
as discussed in Kukush et al. in 2004. The explanatory variable is not always normally …

We generalize the na\" ive estimator of a Poisson regression model with measurement
errors as discussed in Kukush et al.[1]. The explanatory variable is not always normally …