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.

Filter by
Sorted by
Tagged with
0 votes
0 answers
26 views

Quantile regression model when most values are 0- data cleaning or different quantile?

I asked a question on performing quantile regression on a specific dataset and set of variables (which is unanswered as of now- maybe the question isn't as clear.) The following question is somewhat ...
  • 113
1 vote
0 answers
26 views

Quantile regression: getting 0 intercept and coefficients

I have three variables- two independent x1, x2 and one response y1. Scatterplot for the data is, as follows. I want to perform ...
  • 113
3 votes
1 answer
44 views

Generalized quantile regression? Transforming a conditional quantile like we transform conditional expected value

Linear models are models like $\mathbb E\left[Y\vert X\right]=X\beta$. Linear quantile regression models replace $\mathbb E\left[Y\vert X\right]$ with $Q_{\tau}\left(Y\vert X\right)$ for conditional ...
  • 35.8k
0 votes
0 answers
20 views

A Quantile Analysis of the predictive power of the Value factor

Value factor is implemented using three different measures: a. EPQ is the E/P where E is last quarter EPS b. EP12 is the E/P where E is the last 12 months EPS c. BTM is the book to market ratio I need ...
  • 1
0 votes
0 answers
21 views

Which regression models are appropriate for (ratios of) two different but dependent counts?

Consider a variable Y which is the ratio of the frequencies of two words/concepty (f,g) in a text: Y = f/g. Since the texts do not have equal length, the denominator when calculating the percentage of ...
0 votes
1 answer
32 views

Quantile regression necessarily has a solution with $r$ residuals equal to 0: why/how

Given data $\{(Y_i,X_{1,i},X_{2,i}\dots X_{p,i}):1\le i\le n\}$. let $\theta\in(0,1)$ and $\beta=(\beta_1,\beta_2\dots\beta_p)^T$. Then, the quantile regression problem $$\underset{\alpha,\beta}{\min}\...
  • 113
0 votes
0 answers
21 views

How to interpret the quadratic affect in the quantile regression for birth weight example

I have read a nice article about the quantile regression using the brith weight as one example. The article can be accessed via this link Here. The two figures in the second row in page 8 are ...
  • 1,180
2 votes
0 answers
42 views

Predicting percentiles with percentile data [closed]

I have a few independent variables and 5 dependent(target) variables. The target variables are percentiles (10th, 25th, 50th, 75th, 90th) and I want to predict the same in that order. What approach ...
0 votes
0 answers
20 views

Loss function for conditional IQR: is it just the difference between $q_{0.75}$ and $q_{0.25}?$

Quantile regression lets us estimate the conditional first and third quantiles by using particular loss functions that I’ll call $L_{0.25}$ and $L_{0.75}$, respectively. $$ l_{\tau}(y_i, \hat y_i) = \...
  • 35.8k
2 votes
1 answer
678 views

Can a Variable Be Both Dependent and Independent?

We can see that the GDP growth, represented by "y" is the dependent variable and independent variable. I would like to perform quantile regression in Eviews, with ...
  • 161
2 votes
1 answer
72 views

Scikit-learn QuantileRegressor memory allocation error. No issue with statsmodel QuantReg with the same data

I'm trying to fit a quantile regression model to my input data. I would like to use sklearn, but I am getting a memory allocation error when I try to fit the model. The same data with the statsmodels ...
  • 95
1 vote
1 answer
84 views

What is the meaning of a quantile regression model that predicts the conditional mean?

What does that phrase "quantile regression model that predicts the conditional mean" mean? How to interpret that? I found it in Liu et al. (2020). The authors have compared the results of ...
8 votes
3 answers
759 views

Which metric to use to evaluate Quantile Regression?

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 models). If I ...
  • 301
0 votes
0 answers
48 views

Dependent value largely right skewed. What are my options?

I am modeling a dependent variable which is heavily right skewed by a large number of independant variables. This variable is integer. But let's assume this is our model. $ Y = a_0 X_0 + a_1 X_1 + b_0$...
  • 289
1 vote
1 answer
27 views

Using an independent variable correlated with the residuals in quantile regression

If an independent variable correlates with the residuals in quantile regression, does it produce biased coefficient estimates? In ordinary least squares regression, if the independent variable is ...
  • 11
0 votes
0 answers
25 views

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 ...
  • 3
3 votes
1 answer
86 views

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 ...
  • 1,177
4 votes
1 answer
144 views

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: ...
0 votes
0 answers
16 views

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: ...
0 votes
0 answers
113 views

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 ...
  • 850
0 votes
0 answers
23 views

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 ...
  • 5
0 votes
1 answer
93 views

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'...
  • 5
0 votes
0 answers
65 views

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 ...
  • 1
2 votes
0 answers
149 views

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/~...
0 votes
1 answer
99 views

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 ...
0 votes
0 answers
20 views

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 $...
0 votes
2 answers
84 views

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) ...
4 votes
1 answer
397 views

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 ...
  • 823
0 votes
0 answers
20 views

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 ...
  • 337
2 votes
0 answers
51 views

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 ...
  • 281
12 votes
2 answers
430 views

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?
  • 1,027
0 votes
1 answer
52 views

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 ...
  • 1,180
1 vote
0 answers
10 views

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 (...
  • 11
1 vote
0 answers
57 views

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 ...
  • 161
1 vote
0 answers
50 views

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 ...
3 votes
1 answer
95 views

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 ...
3 votes
2 answers
596 views

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) \...
2 votes
1 answer
82 views

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 ...
  • 23
3 votes
1 answer
231 views

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 ...
  • 41
1 vote
0 answers
186 views

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 ...
  • 33
4 votes
1 answer
178 views

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 ...
0 votes
0 answers
21 views

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 <...
  • 233
1 vote
0 answers
84 views

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 ...
  • 1,349
2 votes
1 answer
409 views

(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 ...
  • 532
2 votes
1 answer
249 views

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 ...
5 votes
1 answer
155 views

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, ...
  • 71
3 votes
1 answer
179 views

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 ...
  • 2,836
3 votes
0 answers
57 views

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 ...
  • 890
1 vote
0 answers
322 views

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 ...
  • 53
2 votes
1 answer
458 views

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 ...
  • 53

1
2 3 4 5
8