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), ...
Functional neuroimaging relies on statistical inference to distinguish meaningful patterns of brain activity from high-dimensional, noisy data. Central to most analyses is the mass-univariate general ...
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 ...
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 ...
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 ...
If traffic signals in Tokyo were perfectly optimized, traffic congestion would decrease by 30%. Among urban planners, this ...
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 ...
If program staff suspects you may have used AI tools to complete assignments in ways not explicitly authorized or suspect other violations of the honor code, they will contact you via email. Be sure ...
DA, a post-selection inference framework that provides valid p-values and false positive rate control for semi-supervised ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results