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.

learn more… | top users | synonyms

0
votes
0answers
10 views

How many data points are in a given quantile in Quantile regression?

I hope somebody can help me with a, probably very fundamental, issue of understanding concerning quantile regression. My dataset is very skewed, so I've looking at the data with quantile regression ...
1
vote
0answers
17 views

Should I convert age as independent variable in my Quantile Regression?

I'm applying quantile regression to a dataset where the dependent variable is a measurement of load (utilization) of a specific technology. The model includes a number of independent variables ...
0
votes
0answers
20 views

How does one build a quantile regression model?

I'm confused about how to choose variables for a multivariable quantile regression? Do I just choose significant variables (regardless at what quantile they're significant) and add them to the model? ...
6
votes
1answer
177 views

Expected value as a function of quantiles?

I was wondering where there is a general formula to relate the expected value of a continuous random variable as a function of the quantiles of the same r.v. The expected value of r.v. $X$ is defined ...
1
vote
1answer
23 views

How are the results of multivariable quantile regression interpreted?

Is multivariable quantile regression interpreted the same way as a multivariable linear regression would be interpreted? For example, would I say something like "the coefficient represents the ...
0
votes
0answers
8 views

Error propagation over percentile confidence intervals for bootstrapped regression coefficients

I apologize if this is extremely simple or I'm going about this the wrong way or it has been asked before. Please point me in the right direction if so as I might just be searching the wrong question. ...
0
votes
1answer
29 views

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 ...
0
votes
0answers
11 views

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. ...
11
votes
2answers
564 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 ...
0
votes
0answers
22 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 ...
0
votes
0answers
17 views

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: ...
2
votes
0answers
85 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 ...
1
vote
0answers
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 ...
1
vote
0answers
34 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 ...
0
votes
0answers
11 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) ...
0
votes
0answers
39 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 ...
0
votes
0answers
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 ...
1
vote
2answers
199 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 ...
0
votes
0answers
32 views

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, ...
1
vote
2answers
60 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" ...
0
votes
0answers
24 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 ...
2
votes
0answers
36 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, ...
0
votes
0answers
48 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: ...
1
vote
0answers
70 views

statsmodels: quantreg convergence cycle warning

I am getting the same Convergence cycle detected warning running a quantile regression with ...
0
votes
1answer
65 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 : ...
0
votes
0answers
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 ...
0
votes
0answers
46 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 ...
2
votes
0answers
50 views

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}|]\\ ...
2
votes
2answers
95 views

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 \; ...
12
votes
2answers
832 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 ...
4
votes
0answers
99 views

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 ...
3
votes
2answers
388 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 ...
0
votes
0answers
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 ...
2
votes
1answer
100 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) ...
0
votes
0answers
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.
2
votes
0answers
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 ...
0
votes
0answers
57 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 ...
0
votes
0answers
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.
4
votes
0answers
80 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 ...
0
votes
0answers
37 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 ...
3
votes
2answers
130 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 ...
6
votes
1answer
191 views

Errors in fitting a censored quantile regression model

I have an outcome with right censoring like this: ...
2
votes
0answers
119 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 ...
0
votes
1answer
17 views

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 ...
2
votes
1answer
83 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 ...
3
votes
1answer
97 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 ...
2
votes
1answer
190 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 ...
1
vote
0answers
91 views

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 ...
11
votes
2answers
1k views

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 ...
1
vote
0answers
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 ...