Questions tagged [quantile-regression]

Quantile regression allows us to estimate the effect of a set of predictor variables over the entire distribution of the outcome variable or any particular quantile.

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Time fixed effect - calculate the net of time effect

I have a data that includes two categories, one being Manchester and another Leeds. This is panel data. I have a Year variable, a dummy for city 0 (Man) 1 (Leeds) and a dummy for each Year. How would ...
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n-th quantile for bivariate variable

I generate a 2000 bivariate random samples which are negative correlated. I used np.quantile to generate 10 quantile from this random samples. The related point is marked in the following figure. I am ...
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Theory understanding behind quantile regression

As part of my studies in ecology, I am trying to reproduce a quantile regression method described in Karlsson et. al (2022) (source: https://arxiv.org/pdf/2202.02206.pdf) My starting point is this: ...
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Variations in the output of quantile regression models

I am trying to implement quantile regression models with R for the first time (I am new to this regression approach). Here is the code that I am running: ...
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How to interpret the quantile regression based on random forest?

I have read the Q&A how-are-the-results-of-multivariable-quantile-regression-interpreted and the recommended paper (Petscher and Logan, 2013). I have taken the Wine Quality Data Set, built the ...
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Linear or quantile regression to deal with leverage points?

I'm doing a linear regression model, but even after the log-log transformation it contains many leverage points (outliers that are part of the data), the residuals are not normal and the variance is ...
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How to calculate the correlation by the Spearman method for each quantile of the quantile regression in R?

I'm trying to calculate the Spearman's correlation coefficient for each quantile of my quantile regression (to later calculate the R² of each quantile), but the package I know of (QCSIS) uses Pearson'...
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What are some methods for comparing 2 separate quantile regression curves?

I am currently working with a dataset using the QuantregGrowth package in RStudio to determine the 2.5th/97.5th percentile curves of a biomarker across the age spectrum. I have a pretty good handle on ...
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OLS is BLUE (or BUE) to minimize MSE. Is quantile regression is BLUE to minimize MAE?

The Gauss-Markov theorem considers "best" as "lowest mean square error (MSE)" and a recent version of the theorem shows OLS is not only BLUE but also BUE: https://www.ssc.wisc.edu/~...
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Why is there no panel quantile regression with two-way fixed effects yet?

I have been learning the quantile regression model recently, and my understanding is (possibly wrong) that quantile regression is essentially extracting the subsamples corresponding to a certain ...
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randomforest for multivariate partial cumulative distribution?

https://cran.r-project.org/web/packages/quantregForest/index.html Give a set of features $X_1, X_2, ..., X_n$ (generated according to some process of uncharacterized distribution) and their values $...
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Combining quantile regression with binning

I'm trying to employ a framework where I uncover the marginal effects of the quantiles of one continuous variable on another continuous variable - something analogous to the Quantile-on-quantile (QQR) ...
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Linear Regression and Quantile Regression

Linear regression using the method of least squares estimates the conditional mean of the response variable across values of the predictor variables. Quantile regression estimates a conditional ...
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Estimate quantile coverage

I have implemented, say, a (Bayesian) quantile regression model and I want to assess it by comparing the predicted conditional quantile interval between $Q_1$ and $Q_3$ with the true one (in a ...
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How does the smoothing lambda is calculated in quantreg::rqss?

I have been working with quantreg::rqss() function for non-parametric quantile regression in R (documentation is here). And I have a question of what is happening ...
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Residual using absolute loss linear regression

For ordinary least square linear regression, we have sum of residuals as zero, what about the sum of residuals for linear regression calculated using absolute loss?
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Why "we would be way too confident" while the confidence interval is too small

I read about quantile regression and was confused about one sentence: "We would be way too confident" about confidence interval in this post (under the second figure): here. I think that the ...
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Is it possible to use a Gibbs sampler for uncertainty propagation?

Situation: 10 basic numeric properties are predicted using quantile regression forest, then they are put into a desicion rule system to decide land management. The desicion rules result in one class (...
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Goodness of fit of two models

My two models that I would like to compare are Weibull distribution and Quantile regression (in the case of Weibull distribution I have estimated the quantile curves). I would like to know if there is ...
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Problem in performing quantile regression with quantreg [closed]

I am performing a censored quantile regression on survival data to account for time differences in survival at certain percentiles in my cohort. I am using the ...
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Quantile regression showing different results with same tau

I am using the quantreg package from R to calculate quantile regression between 2 columns : red pixel values and near infrared pixel values (target). But the problem is that it gives me different ...
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Leibniz's Rule for integrals with infinity in the bounds - FOC quantile regression

Hi I came across one application of Leibniz's rule in quantile regression: $$ \frac{\partial E[\rho_\tau(y-c)]}{\partial c} = \frac{\partial}{\partial c} \left[(1-\tau) \int_{-\infty}^{c}(c-y) f_Y(y) \...
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Using kernlab::kqr(reduced = TRUE), how is the y argument missing in the call to csi()?

I'm trying to perform a kernelized quantile regression on some data using the function kqr() from the kernlab package in R. The ...
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Joint hypothesis test for quantile regression in R

I am new to quantile regression and, currently, I am reading a paper that reports results of an F-test for a linear median (quantile) regression. The model is easy and a minimal working example in R ...
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Quantile regression for a sample of city sizes

I would like to estimate the following regression: $$ \ln(Rank) = \alpha + \beta \ln(Population) $$ For an ordered sample of city sizes (from biggest to smallest), where $Rank$ is 1 for the biggest 2 ...
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How to figure out what part of the data cause lad regression not be reach a unique solution?

wget -qO- https://i.stack.imgur.com/AQGsm.gif | tail -c +43 Using the above data (the plot of the data is shown below), I got the following error. ...
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quantile surface of a mulitvariate distribution made of multiplication of marginal distributions assuming independence

How to perform quantile regression in a more elegant fashion? As discussed above, quantSheets() can only deal with one explanatory variable for computing quantile ...
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Best approach to Quantile Vector Autoregression

So I have a system of endogenous variables in panel form (24 Stock over 52 weeks). I would like to use quantile regression of one variable (Trading Volume) upon another (Social Media Sentiment). The ...
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How to perform quantile regression in a more elegant fashion?

https://www.r-bloggers.com/2019/01/quantile-regression-in-r-2/ I see the above method. The regression result is a straight line. But the quantile of the real data may not be on a straight line. The ...
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How many points fall below quantile regression-line?

I am sorry for the relatively easy question regarding quantile regression. I am a little stuck, and reading these resources I can't resolve this problem right now: (1) http://www.econ.uiuc.edu/~roger/...
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Extract slopes for all fitted quantile regression models with different tau-values, R

I wonder how I can get the slopes of all the models that were fitted when I do the following: library(quantreq) z =rq(mpg ~ wt, data=mtcars, tau=-1) Putting the <...
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quantile regression result different from Mann-Whitney U test

The results of the quantile regression and Mann-Whitney U test are very different. My sample size is 39. The quantile regression returns p-value of 0.83, while Mann-Whitney U test gives 0.33. Why are ...
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(rqpd) How to obtain the confidence intervals of a quantile regression model on panel data?

I had to adjust a quantile regression model on panel data and I used the rqdp package, however, when I needed the confidence intervals, I realized that the package does not provide the item in ...
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How to obtain a 0 intercept in quantile regression

Quantile regression models are a type of models that provide estimates of the quantiles of a response variable $y$ given a set of covariates $X$ in the form of a linear equation such as $$ y = \beta_0 ...
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How does ordinal regression compare to quantile regression?

I am familiar with ordinal regression and quantile regression at a high level, but would like a deeper understanding of the two beginning on how they differ. Can someone compare and contrast the two, ...
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How to get main effects (deviance table) on the censored quantile regression in R quantreg::crq?

I know it's about R, but it's anyway a statistical question. I want to calculate confidence intervals for the survival quantiles (median and the 1st quartile). I was asked to use namely the censored ...
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Quantile regression on a constant: is this different from unconditional quantile?

With a linear model, estimating an OLS regression of y on a constant only will give us the mean of y. I was wondering whether ...
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Is it possible to specify different quantile regression models for each quantile?

As the title says. I have never seen it, but I see no point that would prohibit me to do it. For example, a different set of variables might bear predictive value for the 25th-percentile of the ...
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Hyperparameter tuning of quantile gradient boosting regression and linear quantile regression

I have am using Sklearns GradientBoostingRegressor for quantile regression as wells as a linear neural network implemented in Keras. I do however not know how to find the hyperparameters. For the ...
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Hyperparameter tuning of statsmodels quantile regression

I am working in statsmodels and I am trying to do linear quantile regression with the function QuantReg. I can however not figure out a way to tune any ...
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Hyperparameter tuning of gradient boosting and neural network quantile regression

I have am using Sklearns GradientBoostingRegressor for quantile regression as wells as a nonlinear neural network implemented in Keras. I do however not know how to find the hyperparameters. For the ...
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Granger causality testing with rq() quantile regression

I am trying to test for Granger causality in my quantile regression. I have x (=position data) and y (=price data) in a dataframe called dat and then employ ...
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Summation of median and quantiles of multiple forecasted variables

Assume that I have Y1_hat with its P10_1 and P90_1 and Y2_hat with its P10_2 and P90_2. Is it valid to sum Y1_hat and Y2_hat, sum P10_1 and P10_2, and sum P90_1 and P90_2? and would that present any ...
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Quantile regression with an exponential function

The following equation: y = a*x**b where y is a nonlinear function of x. By taking logs, the equation can be expressed as: ln(y) = ln(a) + bln(x). I would like to run a quantile regression instead of ...
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Which tests should be perfomed after quantile regressions have been estimated?

I´m performing a quantile regression. Initially I opted for a linear regression, but as I suspected that variations in X had different effects on the outcome variable across the distribution, I ...
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How to understand the coefficients from regression at different quantiles. Do they correlated with each other?

I applied quantile regression on this example data: t<- data.frame(id=seq(1,100), measureX =rnorm(100,0,1),measureY = rnorm(100,0,2)) This is the code I used: <...
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consistent estimation of quantiles (without overlapping quantiles)

I would like to forecast quantile ranges. The observations are assumed to be heteroscedastic. Mostly, I am confronted with the problem that quantile regression results for different quantiles do ...
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Quantile interpretation

If I have have the score for $q=0.9$, say $Y=y_{0.9}$. Does this mean that the probability of measuring a score $<y_{0.9}$ is 0.9? Edit: Basically I have one dependent variable, $y$ (a type of ...
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Quantile Regression Median place within bounds

In quantile regression I understand that I am estimating prediction intervals with an upper and lower quantile and that the .5 is supposed to be the median. However I have encountered a situation ...
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Bias in Quantile Regression

What are the assumption for the quantile regression estimator to be unbiased? I looked up Koenker 2005 but only found a theorem on consistency.
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