Given the potentially strong, adverse effects of measurement error and the possibility of minimizing these using Hidden Markov models (HMMs), the aim of this thesis ...

Data that are collected for the production of official statistics or, more generally, for statistical analyses nearly always contain measurement errors. National statistical institutes, other ...

We derive exact finite-sample expressions for the biases and risks of several common pretest estimators of the scale parameter in the linear regression model. These estimators are associated with ...

Scientists are evaluating machine-learning models using transfer learning principles. Omar Maddouri, a doctoral student in the Department of Electrical and Computer Engineering at Texas A&M University ...

We address the problem of upper bounding the mean square error of MCMC estimators. Our analysis is nonasymptotic. We first establish a general result valid for ...

Nonlinear estimation algorithms are required for obtaining estimates of the parameters of a regression model with innovations having an ARMA structure. The three estimation methods employed by the ...