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
5 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
20 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
29 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
74 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
20 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
46 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
13 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
23 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
22 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
35 views

statsmodels: quantreg convergence cycle warning

I am getting the same Convergence cycle detected warning running a quantile regression with ...
0
votes
1answer
55 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
24 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
40 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
42 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
84 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 \; ...
11
votes
2answers
464 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
83 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
217 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
39 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
84 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
27 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
44 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
50 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
73 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
34 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
107 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 ...
0
votes
0answers
31 views

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 ...
0
votes
0answers
29 views

Different models for different quantile functions possible?

My aim is to estimate the 2.5%- and 97.5%-quantile function (to get reference intervals) for a specific score in dependence of age separated by classes of a third variable cag. So first I built 11 ...
6
votes
1answer
155 views

Errors in fitting a censored quantile regression model

I have an outcome with right censoring like this: ...
2
votes
0answers
99 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
57 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
80 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 ...
0
votes
0answers
81 views

Quantile regression cross validation

In a previous question i asked how to calculate the quantiles of my data so that i can do cross validation on a hold out set of data for my quantile regression model. But i think i understood what i ...
2
votes
1answer
163 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
77 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 ...
10
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
41 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 ...
0
votes
0answers
340 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 ...
1
vote
1answer
156 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?
1
vote
1answer
63 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 ...
7
votes
1answer
165 views

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}< ...
1
vote
0answers
56 views

Figuring out quantiles in quantile regression

Suppose I have a dataset $\{y_i,x_i\}$ $i=1,2,...n$. For the response variable, $y_i$ as per quantile regression I have the following likelihood: $$p(y_i|\beta,\alpha_i,\sigma) ...
0
votes
1answer
41 views

Applying ensemble learning to quantile regression?

Is it desirable / possible to apply ensemble learning methods (boosting, bagging, etc) to the quantile regression problem?
5
votes
1answer
168 views

Quantile regression power analysis

New to the site and to stats here! This may be a silly question, but I haven't been able to find a satisfactory answer on the procedure for a power analysis (or general guidelines about sample size) ...
16
votes
0answers
646 views

What is the difference between conditional and unconditional quantile regression?

The conditional quantile regression estimator by Koenker and Basset (1978) for the $\tau^{th}$ quantile is defined as $$ \widehat{\beta}_{QR} = \min_{b} \sum^{n}_{i=1} \rho_\tau (y_i - X'_i b_\tau) $$ ...
0
votes
1answer
137 views

Huber sandwich estimator in quantile regression

I need the description of Huber sandwich estimate method for quantile regression. I found this "a Huber sandwich estimate using a local estimate of the sparsity function". Sparsity function looks ...
0
votes
0answers
49 views

Mean of residuals in quantile regression are significantly differ from 0

Is it necessary to have mean of residuals which is equal to 0 in Quantile regression?
3
votes
1answer
305 views

Quantile regression vs. Li's regression: which should I use, and when?

Is there a general rule of thumb about when robust regression or quantile regression is preferred in the presence of outliers? For example, I have a dataset where the DV exhibits extreme positive ...