Feb 9, 2020 · Quantile regression is something different, where the conditional median is estimated (for $\tau = 0.5$) or at any other percentile of interest. Which percentile depends on …

Jun 27, 2022 · I have a prediction problem for which I want to predict the 75% Quantile using Quantile Regression. I am a little bit confused on how to evaluate this model (and also …

Dec 22, 2012 · The summary.rq function from the quantreg vignette provides a multitude of choices for standard error estimates of quantile regression coefficients. What are the ...

Jul 8, 2015 · How does quantile regression "work"? Ask Question Asked 10 years, 5 months ago Modified 3 years, 5 months ago

Nov 17, 2019 · The DHARMa package in R aims to provide scaled (quantile) residuals that, according to the DHARMa vignette, "can be interpreted as intuitively as residuals from a linear …

Feb 19, 2025 · If you are interested in testing whether the slopes are the same over different quantile levels for all variables in the model, I would suggest doing the joint test. You could …

Nov 17, 2022 · With that in mind, the typical loss function for quantile regression, much like how the typical loss function for linear regression is the sum of squared residuals, is the quantile …

An intuitive proof of this fact can be found in Quantile Regression (2005), Section 1.3, by Roger Koenker. In view of (1) (1), the τ τ -th sample quantile receives a new interpretation as the …

Dec 29, 2020 · The quantile regression estimators for different quantiles are asymptotically distributed as a multivariate normal random vector with a certain mean vector and a certain …

Jul 29, 2023 · The goal of quantile regression is to estimate the parameters $\beta (\tau)$ for a given value of $\tau$. I am trying to understand: For what kinds of problems is Quantile …