Measurement error models address the deviation between observed and true values, thereby refining the reliability of statistical inference. These frameworks are ...

Consider a statistical model depending upon the parameters θ and ψ. Statistical procedures for drawing inference on θ (the structural parameter) can be based on a submodel obtained by a conditioning ...

Statistics is a branch of math that involves the collection, description, analysis, and inference of conclusions from ...

This online data science specialization is designed to provide you with a solid foundation in probability theory in preparation for the broader study of statistics. The specialization also introduces ...

Bayesian statistics represents a powerful framework for data analysis that centres on Bayes’ theorem, enabling researchers to update existing beliefs with incoming evidence. By combining prior ...

This is a preview. Log in through your library . Abstract We consider challenges that arise in the estimation of the mean outcome under an optimal individualized treatment strategy defined as the ...

"In this universe effect follows cause. I've complained about it, but. . ." -- House (Laurie), pre-sponding to D. Bem "The more extraordinary the event, the greater the need for it to be supported by ...

In the 21st century, artificial intelligence (AI) has emerged as a valuable approach in data science and a growing influence in medical research, 4-6 with an accelerating pace of innovation. This ...