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 also test these …

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 the value of $\tau$ …

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

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 Regression better …

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 loss ("pinball …

Feb 28, 2023 · In summary, the question " Is quantile regression a maximum likelihood method? " is somewhat ill-posed because the former is a statistical model while the latter is a parameter …

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 compare different …

Jan 2, 2024 · My office leadership is interested adopting “quantile time series forecasting”, the idea is query the model to predict the 5th, 25th, 50th, 75th and 95th percentiles of an RV given features …

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 ...

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 minimizer …