Plotting Distributions in R – Jason Bryer

Function and Shiny application for working with distributions in R.

KNIME for Finance: Fraud detection using quantiles | KNIME

Learn to use quantiles in KNIME to easily identify fraudulent activity in huge volumes of transaction data. Download the workflow in the article to try it yourself.

QUANT: A Minimalist Interval Method for Time Series Classification

We show that it is possible to achieve the same accuracy, on average, as the most accurate existing interval methods for time series classification on a standard set of benchmark datasets using a single type of feature (quantiles), fixed intervals, and an 'off the shelf' classifier. This distillation of interval-based approaches represents a fast and accurate method for time series classification, achieving state-of-the-art accuracy on the expanded set of 142 datasets in the UCR archive with a total compute time (training and inference) of less than 15 minutes using a single CPU core.

Expected Pinball Loss For Quantile Regression And Inverse CDF...

We analyze and improve a recent strategy to train a quantile regression model by minimizing an expected pinball loss over all quantiles. Through an asymptotic convergence analysis, we show that...

Flexible Model Aggregation for Quantile Regression

Bounded Space Differentially Private Quantiles

Estimating the quantiles of a large dataset is a fundamental problem in both the streaming algorithms literature and the differential privacy literature. However, all existing private mechanisms...

Bounded Space Differentially Private Quantiles

Estimating the quantiles of a large dataset is a fundamental problem in both the streaming algorithms literature and the differential privacy literature. However, all existing private mechanisms...