Estimation methods form the backbone of statistical inference, allowing researchers to infer unknown parameters from observed data. Classical approaches include the maximum likelihood estimator (MLE), ...

A new framework called FLARE combines nonlinear machine learning flexibility with closed-form uncertainty estimates, ...

Many would probably be content to use Bayesian methodology for hypothesis testing, if it was easy, objective and with trustworthy assumptions. The Bayesian information criterion and some simple bounds ...

Over the past several decades, multiple statistical methods have been developed to infer the existence and magnitude of causal effects by analyzing observational data. These methods have been widely ...

Our foray into causal analysis is not yet complete. Until we define the methods of causal inference, we can't get to the deeper insights that causal analysis can provide. This article details many of ...

In the 21st century, we have seen a growing availability of shape-valued and imaging data, prompting the development of new statistical methods to analyze them. Importantly, bridging the new methods ...

"Since 2020, rural real estate prices have been rising"—this 'observation' is correct. However, the 'causality' that ...