The era of predictive modeling enhanced with machine learning and artificial intelligence (AI) to aid clinical ...

Background Coarctation of the aorta (CoA) has good modern results, but large multicentre longitudinal data on outcomes, especially hospital resource utilisation through childhood and adolescence, are ...

But in neural network quantile regression, you must also watch the current quantile value to make sure you approach the desired quantile percentile value. Evaluating and Using the Neural Network ...

To address systematic batch effects, we apply a negative binomial regression model and correct for consistent batch influences by excluding fixed batch effects. Additionally, to handle nonsystematic ...

This paper critically examines ‘kitchen sink regression’, a practice characterised by the manual or automated selection of variables for a multivariable regression model based on p values or ...

With that end in mind I have been trying to reproduce the stock quantile regressor results, I want to begin with reproducing stock behaviour so I know I have a good foundation to start modifying the ...

In quantile regression models, numerous penalization methods have been developed to deal with ordinary least-squares method problems. Such methods are ridge penalized quantile regression, lasso ...