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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How to plot quantile regression with LASSO in R? [on hold]

Good day! Please help me with plotting quantile regression with LASSO in R. Here are the codes I used. library(rqPen) y<- read.csv("C:\Users\book1.csv", header=F, col.name=c("WD")) ...
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How do you do a nonparametric quantile regression in SAS?

I want to do a nonparametric quantile regression in SAS and I can't, for the life of me, figure out how to do it. All the examples I see don't do a good job of explaining the code that is used and why ...
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Quantile or distribution estimation for continuous variable from sparse matrix

I'm not sure where to start and desperatley need help. I've got a somewhat sparse data set and I'm trying to do either a quantile estimation or a distribution estimation for one continuous variable. ...
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547 views

How does quantile regression “work”?

I am hoping to get an intuitive, accessible explanation of quantile regression. Let's say I have a simple dataset of outcome $Y$, and predictors $X_1, X_2$. If, for example, I run a quantile ...
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19 views

Can quantile regression be used to pool multiply imputed count data?

I am using the mice package in R to impute missing data in small study. The study investigates the effect of a behavioral intervention on the frequency of a particular behavior, i.e., count data that ...
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running quantile regression on data with several factor levels in r

I am trying to run a quantile regression on a dataset like the following: ...
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60 views

Empirical Prediction interval for time series forecast based on quantile regression

As Gardner notes "almost all point forecasts are wrong", so prediction intervals (PI) are necessary to quantify uncertainty and help us make informed decisions. There exists theoretical PI, and in ...
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26 views

Heteroscedastic censored regression

I am dealing with a heteroscedastic censored dataset. I tried to use the survival analysis package in R to estimate a linear model for it. So before doing that, I conducted a simulation study, where I ...
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29 views

Is it possible to get a prediction interval for logistic regression via a latent variable?

carbocation asked how to compute prediction intervals for logistic regression. The answer was that prediction intervals don't make sense for logistic regression because the response variable only ...
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10 views

change co-variance structure for linear quantile mixed model for animal breeding

I'd like to analysis my data (animal breeding) with linear quantile mixed model. Lqmm package in R does that but co-variance structure do not know A (relationship numerator matrix is Sparse matrix) ...
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35 views

Quantile regression prediction in r

When I call predict.rq function, it returns a matrix which stored different predictions when in different quantiles. But I only need exactly one number for each ...
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34 views

About the appropriate regression model

I have a quantitative dependent variable and all my explanatory variables are qualitative (binary or multi-category). I need to analyze the impact of each level on the dependent variable. I also need ...
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2answers
134 views

Independent and Dependent variables use different scales

How to deal with questionnaire, where 40 questions that represent 8 independent constructs use 5-point Likert's scales and another 5 questions that represent dependent variable use 6-points Likert's ...
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Quantile regression and panel data

I’m interested in the estimating the effect on an explanatory variable along the distribution (quantiles) of a dependent variable. I am aware that quantile regression will allow me to do so. However, ...
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55 views

truncated quantile regression in R

I have used the "quantreg" package in R to find quantiles for my data. All my data, both predictors and responses are limited between 0 and 1, while a number of quantiles given by "rq" or "rqss" ...
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22 views

Interpretation of the confidence ellipsoids of multivariate distributions when they are transferred to their original univariate distributions

I borrow an simple example from this link (68% Confidence level in multinormal distributions ) I wonder how x1 and x2 values which satisfy the ellipse equation can be interpreted if they are ...
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32 views

incremental quantile regression

I am reading about quantile regression. I wonder if there is a way to incorporate new data into the regression model and update the parameters on the fly. [1] seems to propose a similar idea, however, ...
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35 views

Bandwidth and Sparsity in Quantile Regression

I am struggling to understand what bandwidth and sparsity mean in the context of quantile regressions and how they relate to the pseudo r-squared. This is what I get: ...
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63 views

statsmodels: quantreg convergence cycle warning

I am getting the same Convergence cycle detected warning running a quantile regression with ...
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61 views

quantile regression question [closed]

How can we guess which one of a quantile regression equation between several quantile is better and we can choose that.for example if we have 2 equation : ...
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28 views

Using quantile regression with a linear equation bounded by 1

I am trying to replicate the research of Ducey and Knapp (2010)* with my own data using the quantreg package in R. My question/problem might be algebraic rather than statistical, but I thought I would ...
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44 views

Are there multiple ways to interpret the slope parameters in linear regression?

I am struggling to understand an interpretation of regression parameters presented in a paper comparing and contrasting OLS regression to quantile regression. The authors present an example linear ...
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A question in proof from “Regression Quantiles” by Roger Koenker and Gilbert Bassett(Econometrica, 1978)

I have a problem in verifying the conclusion (3.5) in proving Theorem 3.3 as attached here. Particularly, from (3.4) \begin{align} 0&<\sum_{k=1}^{K}[(1/2-\theta)v_{k}+|v_{k}|]\\ ...
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A question in directional derivatives of a quantile regression object function

The question comes from the paper ``Regression Quantiles'' by Roger Koenker and Gilbert Bassett(Econometrica, 1978). $0< \theta <1$. Define $\psi(b;\theta,y,X)=\sum^{T}_{t=1}[\theta-1/2+1/2 \; ...
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735 views

r-squared in quantile regression

I am using quantile regression to find predictors of 90th percentile of my data. I am doing this in R using the quantreg package. How can I determine $r^2$ for ...
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Testing for statistical differences of quantile regression line slopes

If I were to compare the statistical similarity between the slopes of OLS regression lines from two independent samples, I would use a t-test to test if the slopes are equal or not. I'd like to ...
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326 views

Is it valid to use quantile regression with only categorical predictors?

I am new to quantile regression and most of the examples I see are in a multiple regression context with continuous predictors. I am analyzing a designed experiment and was wondering if quantile ...
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42 views

Posterior simulations in quantile regression

I'm fitting quantile regression models and would like to do posterior simulations from the fitted models, i.e. generating new random data which could arise from the model. I would know how to do this ...
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95 views

Marginal Effects of Discrete Variables in Quantile Regression

I find myself puzzled by a passage about marginal effects of discrete variables in quantile regression. On p. 217 of Cameron and Trivedi's MUS book, the authors write: For the $j$th (continuous) ...
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30 views

GAM-style effects plots for interpreting qrnn model

how to analyse GAM-style effects plots for interpreting qrnn models. I couldn't quite understand it from R documentation.
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48 views

Hausman test for quantile regressions

I would like to know whether I can compare instrumental variables quantile estimates, for instance using the the Chernozhukov and Hansen (2006) IVQR estimator (or quantile regression with fixed ...
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54 views

Comparing medians

I am interested in comparing the medians of a continuous random variable across two groups. One option is to do a chi-square-based median test - in stata run median var, by(group). The other option is ...
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21 views

How to Predict using Quantile Regression [duplicate]

Were you able to figure out your question about what quantile prediction to use for the forecasting purposes? Different quantile regressions produce different estimates.
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78 views

Quantile regression with censored data. Quantiles not fitted

I'm trying to fit a quantile regression model for rigth censoring data and I'm using R with the package quantreg and its function crq. I'm trying the Portnoy method that it's suposed to estimate the ...
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36 views

Interpretation of quantile regression with discrete variables, whole sample vs subsample

I am trying to run a (conditional) quantile regression, my outcome $(Y)$ is income, and my regressors $(X)$ are all discrete. My question is what are the differences if, when I focus on one particual ...
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125 views

Non-linear least absolute deviation regression with multiple global minima

I am fitting a single exponential decay formula with three parameters (a,b,c): y ~ $a \exp(-xb) + c$ using the LAD cost function: $ \min \sum |(y - f(x))| $. $x$ is in units of time (as is $b$), and ...
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How to forecast with quantile regressoin

If you have three quantile regression models with taus of 0.25, 0.5 and 0.75 and their coefficients how do you use these models to forecast a set of data not used to calculate the coefficients. In ...
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186 views

Errors in fitting a censored quantile regression model

I have an outcome with right censoring like this: ...
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116 views

Cross validating quantile regression

I applied quantile regression on some data and did it for tau = 0.25, 0.5, 0.75. After i got the estimates of each model, i did some cross validation on my hold out data. When i used the estimates for ...
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Uncertainty analysis

Here is my situation. I am trying to predict the 'entire' distribution of the dependent variable, not just the mean( or conditional mean). Does it then make sense to seprateley predict quantiles of ...
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1answer
81 views

Regarding the quantile regression via optimization approach

The quantile regression is defined through the optimization approach. But I am not clear how does the function of $\rho_{\tau}(u)$ related to the $\tau$-th quantile. Or in other words, how to derive ...
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94 views

Coefficient standard error of zero in quantile regression

I'm currently experimenting with quantile regression of a strongly right skewed outcome variable y on a 3-category exposure x (values 1,2,3). I wanted to model the .2, .5, and .8 quantile, using the ...
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187 views

Quantile regression forecast accuracy

I am doing a quantile regression in R with some data and then i want to test the accuracy of the coefficients on a another data set (hold out set). But i am not sure how to go about measuring the ...
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Comparing results from linear quantile regression with mean regression

Say we fitted quantile regression models with a set of quantiles between 0 and 1 and a linear regression (i.e. mean regression) to a same dataset with the same set of covariates. In terms of the ...
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Literature on IV quantile regression

In the last months I have read intensively about quantile regression in preparation for my master thesis this summer. Specifically I have read most of Roger Koenker's 2005 book on the topic. Now I ...
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45 views

Nonparametric Time Series Forecasting

I am trying to understand how Kernel Density Estimation (KDE) or (nonparametric) Quantile Regression can be used to forecast values given historical observations. For example, consider the following ...
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379 views

Predicted values for fixed effect quantile regression

I'm currently working with the method proposed by Koenker (2004) and Lamarche(2010) on fixed effects for quantile regression, for this I'm using the RQPD code in R. I would like to get the predicted ...
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172 views

OLS vs. quantile regression

I ran OLS regression in Stata. Based only on the results I got in OLS, is there any way to know if the quantile regression will be a better choice?
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71 views

What can be inferred when multivariable ordinary least squares and quantile (median) regression yield differing results?

There lies information in a discrepancy of the (unconditional) mean and median. For example, if the median is larger than the mean, the distribution must be left-skewed. Does this kind of inference ...
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Quantile regression estimator formula

I have seen two different representations of the quantile regression estimator which are $$Q(\beta_{q}) = \sum^{n}_{i:y_{i}\geq x'_{i}\beta} q\mid y_i - x'_i \beta_q \mid + \sum^{n}_{i:y_{i}< ...