The goal of a machine learning regression problem is to predict a single numeric value. Quantile regression is a variation where you are concerned with under-prediction or over-prediction. I'll phrase ...

Estimating the conditional quantiles of outcome variables of interest is frequent in many research areas, and quantile regression is foremost among the utilized methods. The coefficients of a quantile ...

One of the more difficult challenges for modeling is deciding how (or if) to deal with extreme data points. It’s a common problem in economic and financial numbers. Fat tailed distributions are ...

The SiZer methodology proposed by Chaudhuri and Marron (1999) is a valuable tool for conducting exploratory data analysis. Since its inception different versions of SiZer have been proposed in the ...

Immunotherapy has been approved to treat many tumor types. However, one characteristic of this therapeutic class is that survival benefit is due to late immune response, which leads to a delayed ...

After dropping 3.45% on Sept. 30, Bitcoin (BTC) missed out on a monthly bullish engulfing candle for the first time since January 2023. Still, a year-long bull flag remains in play for Bitcoin, which ...

However, a relatively new form of quantile regression is neural network quantile regression -- a variation of neural network regression. By using a custom loss function that penalizes low predictions ...