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I am trying to predict the 10th, 30th, 50th, 70th and 90th quantiles of dependent variable. I have 110 independent variables in the data set. I can think of two approaches to do this.

  1. Use all data points and then predict above quantiles using quantile regression.
  2. First subset data into 5 groups i.e. [0-20], (20-40], (40-60],(60-80] and (80-100] and then predict median value for each group.

I am ultimately interested in coefficient values of independent variables at different quantiles.

With approach 1 - I mostly see either increasing or decreasing trend of coefficients from 10th quantile to 90 quantile.

I do not see such trend with second approach. However, what could be the potential pitfalls of second approach.

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  • $\begingroup$ Have you tried doing some basic exploratory data analysis? Surely, there are a few variables that can be immediately dropped out from that process. I mean, you can at least look at some simple correlations. $\endgroup$ – Jon Jan 12 '18 at 20:22
  • $\begingroup$ I have checked VIF and removed highly correlated features. 110 columns is after doing all necessary checks. $\endgroup$ – Chandra Jan 12 '18 at 20:31
  • $\begingroup$ If you've checked VIF's, then I assume you've at least run 1 linear regression model. If you did, you probably saw the coefficients and p-values. I would inspect those with low effect sizes and/or high p-values to see if there is any significant relationship. If not, drop them. Model building is not an easy process; it's tedious work. $\endgroup$ – Jon Jan 12 '18 at 21:21
  • $\begingroup$ @Jon, thank you for responding. My question does pertain to feature selection. It is regarding predicting different quantile values. I am seeking answer to identify whether predicting median after classification is same as predicting different quantiles using quantile regression. $\endgroup$ – Chandra Jan 12 '18 at 21:25

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