Health researchers need to fully understand the underlying assumptions to uncover cause and effect. Timothy Feeney and Paul Zivich explain Physicians ask, answer, and interpret myriad causal questions ...

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...

Abstract: We study sparse group Lasso for high-dimensional double sparse linear regression, where the parameter of interest is simultaneously element-wise and group-wise sparse. This problem is an ...

Stochastic gradient descent (SGD) provides a scalable way to compute parameter estimates in applications involving large-scale data or streaming data. As an alternative version, averaged implicit SGD ...

Economists say unbiased data is essential for policymaking, and for democracy. President Trump said he ousted the head of the Bureau of Labor Statistics because the numbers produced by her agency were ...

So I have assumed that the modality should be "MRI", but when I run the code above from the inference_examples_RGB.ipynb, it says that the MRI modality is not part of the modalities they support.

Since the Chinese company’s chatbot surged in popularity, researchers have documented how its answers reflect China’s view of the world. Some of its responses amplify propaganda Beijing uses to ...

Abstract: The problem of statistical inference in its various forms has been the subject of decades-long extensive research. Most of the effort has been focused on characterizing the behavior as a ...

Repeated measurements of the same countries, people, or groups over time are vital to many fields of political science. These measurements, sometimes called time-series cross-sectional (TSCS) data, ...

Jeffrey Johnson has written novels and movies in addition to legal analyses of eminent domain and immigration law. His experience in writing engaging fiction makes him uniquely capable of making the ...