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

Jul 8, 2015 · How does quantile regression "work"? Ask Question Asked 10 years, 6 months ago Modified 3 years, 6 months ago

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 …

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 …

Feb 28, 2023 · Quantile regression allows to estimate a conditional quantile for y (like e.g. the median of y,...) from data x. I do not see any distributional assumptions about y being made. …

Dec 22, 2012 · I hope you get many responses to this excellent question. We need some guidance in this area. Another approach, made easy by the R rms package's bootcov function …

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 …

Nov 25, 2020 · I fit a quantile regression using quantreg:::rq on clustered data. I use the Huber sandwich estimator to obtain cluster-corrected standard errors, which is indicated by the se = …

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 …

Feb 10, 2024 · Then, the upper confidence interval will be the $95\%$ quantile from the bootstrapped values. But if you have a better modification of bootstrap in mind, let me know. …