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Frank Harrell
Frank Harrell
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Quantile regression assumes

  • the normal regression assumptions of linearity and additivity (unless you add more terms to the model)
  • independence of observations
  • very large sample size, as quantile regression is not very efficient
  • $Y$ is very continuous; quantile regression doesn't work well when there are many ties at one or more values of $Y$

You might also consider semiparametric regression (e.g., proportional odds or hazards models) which are more efficient and also allow you to estimate the mean.

My RMSRMS course notes goes a bit more into quantile and semiparametric regression in the chapter on ordinal models for continuous $Y$.

Quantile regression assumes

  • the normal regression assumptions of linearity and additivity (unless you add more terms to the model)
  • independence of observations
  • very large sample size, as quantile regression is not very efficient
  • $Y$ is very continuous; quantile regression doesn't work well when there are many ties at one or more values of $Y$

You might also consider semiparametric regression (e.g., proportional odds or hazards models) which are more efficient and also allow you to estimate the mean.

My RMS course notes goes a bit more into quantile and semiparametric regression in the chapter on ordinal models for continuous $Y$.

Quantile regression assumes

  • the normal regression assumptions of linearity and additivity (unless you add more terms to the model)
  • independence of observations
  • very large sample size, as quantile regression is not very efficient
  • $Y$ is very continuous; quantile regression doesn't work well when there are many ties at one or more values of $Y$

You might also consider semiparametric regression (e.g., proportional odds or hazards models) which are more efficient and also allow you to estimate the mean.

My RMS course notes goes a bit more into quantile and semiparametric regression in the chapter on ordinal models for continuous $Y$.

added more info as requested by follow-up question
Source Link
Frank Harrell
Frank Harrell
  • 98.4k
  • 6
  • 191
  • 448

Quantile regression assumes

  • the normal regression assumptions of linearity and additivity (unless you add more terms to the model)
  • independence of observations
  • very large sample size, as quantile regression is not very efficient
  • $Y$ is very continuous; quantile regression doesn't work well when there are many ties at one or more values of $Y$

You might also consider semiparametric regression (e.g., proportional odds or hazards models) which are more efficient and also allow you to estimate the mean.

My RMS course notes goes a bit more into quantile and semiparametric regression in the chapter on ordinal models for continuous $Y$.

Quantile regression assumes

  • the normal regression assumptions of linearity and additivity (unless you add more terms to the model)
  • independence of observations
  • very large sample size, as quantile regression is not very efficient
  • $Y$ is very continuous; quantile regression doesn't work well when there are many ties at one or more values of $Y$

You might also consider semiparametric regression (e.g., proportional odds or hazards models) which are more efficient and also allow you to estimate the mean.

Quantile regression assumes

  • the normal regression assumptions of linearity and additivity (unless you add more terms to the model)
  • independence of observations
  • very large sample size, as quantile regression is not very efficient
  • $Y$ is very continuous; quantile regression doesn't work well when there are many ties at one or more values of $Y$

You might also consider semiparametric regression (e.g., proportional odds or hazards models) which are more efficient and also allow you to estimate the mean.

My RMS course notes goes a bit more into quantile and semiparametric regression in the chapter on ordinal models for continuous $Y$.

Source Link
Frank Harrell
Frank Harrell
  • 98.4k
  • 6
  • 191
  • 448

Quantile regression assumes

  • the normal regression assumptions of linearity and additivity (unless you add more terms to the model)
  • independence of observations
  • very large sample size, as quantile regression is not very efficient
  • $Y$ is very continuous; quantile regression doesn't work well when there are many ties at one or more values of $Y$

You might also consider semiparametric regression (e.g., proportional odds or hazards models) which are more efficient and also allow you to estimate the mean.