Welcome to the D-FINE repository! This project aims to redefine the regression task of DETRs (DEtection TRansformers) as fine-grained distribution refinement. Our work will be presented at ICLR 2025 ...

In traditional models like linear regression and ANOVA, assumptions such as linearity, independence of errors, homoscedasticity, and normality of residuals are foundational.

The regression function is a fundamental object in classification as it determines both the Bayes optimal classifier and the misclassification probabilities. A resampling based framework is presented ...

Discover what standard error reveals about regression precision and why it's essential for accurate statistical modeling.

When you delve into regression analysis in Business Intelligence (BI), you'll encounter the term 'standard error' (SE). This is a measure of the accuracy with which a sample distribution ...

However, traditional methods based on quantile regression estimation can lead to inadequate risk estimates. Therefore, we propose a method based on the Conditional Autoregressive Value at Risk (CAViaR ...

This paper studies the expectile regression with error-in-variables to reduce the data error and describe the overall data distribution. Specifically, the asymp ...

This study investigated the effects of various seasonal fitting techniques on the spatial distribution of the common mode errors taking the coordinate time series of the continuous GPS reference ...