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How does quantile regression compare to logistic regression with the variable split at the quantile?

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5

1

$begingroup$

I googled a bit but didn't find anything on this.

Suppose you do a quantile regression on the qth quantile of the dependent variable.

Then you split the DV at the qth quantile and label the result 0 and 1. Then you do logistic regression on the categorized DV.

I'm looking for any Monte-Carlo studies of this or reasons to prefer one over the other etc.

logistic quantile-regression

share|cite|improve this question

asked 3 hours ago

Peter Flom♦Peter Flom

77k12109215

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Could you show us any reasonable way even to compare the results of the two regressions? After all, unless you have something a little less general in mind, the coefficients of the regressors in these two models have entirely different meanings and interpretations, so in what sense are we to understand what you mean by "prefer"?
  $endgroup$
  – whuber♦
  1 hour ago
  

  
  
  
  

add a comment | 

5

1

$begingroup$

I googled a bit but didn't find anything on this.

Suppose you do a quantile regression on the qth quantile of the dependent variable.

Then you split the DV at the qth quantile and label the result 0 and 1. Then you do logistic regression on the categorized DV.

I'm looking for any Monte-Carlo studies of this or reasons to prefer one over the other etc.

logistic quantile-regression

share|cite|improve this question

asked 3 hours ago

Peter Flom♦Peter Flom

77k12109215

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Could you show us any reasonable way even to compare the results of the two regressions? After all, unless you have something a little less general in mind, the coefficients of the regressors in these two models have entirely different meanings and interpretations, so in what sense are we to understand what you mean by "prefer"?
  $endgroup$
  – whuber♦
  1 hour ago
  

  
  
  
  

add a comment | 

$begingroup$

I googled a bit but didn't find anything on this.

Suppose you do a quantile regression on the qth quantile of the dependent variable.

Then you split the DV at the qth quantile and label the result 0 and 1. Then you do logistic regression on the categorized DV.

I'm looking for any Monte-Carlo studies of this or reasons to prefer one over the other etc.

logistic quantile-regression

share|cite|improve this question

asked 3 hours ago

Peter Flom♦Peter Flom

77k12109215

$endgroup$

share|cite|improve this question

asked 3 hours ago

Peter Flom♦Peter Flom

77k12109215

share|cite|improve this question

asked 3 hours ago

Peter Flom♦Peter Flom

77k12109215

asked 3 hours ago

Peter Flom♦Peter Flom

77k12109215

asked 3 hours ago

Peter Flom♦Peter Flom

77k12109215

1 Answer
1

active

oldest

votes

4

$begingroup$

For simplicity, assume you have a continuous dependent variable Y and a continuous predictor variable X.

Logistic Regression

If I understand your post correctly, your logistic regression will categorize Y into 0 and 1 based on the quantile of the (unconditional) distribution of Y. Specifically, the q-th quantile of the distribution of observed Y values will be computed and Ycat will be defined as 0 if Y is strictly less than this quantile and 1 if Y is greater than or equal to this quantile.

If the above captures your intent, then the logistic regression will model the odds of Y exceeding or being equal to the (observed) q-th quantile of the (unconditional) Y distribution as a function of X.

** Quantile Regression**

On the other hand, if you are performing a quantile regression of Y on X, you are focusing on modelling how the q-th quantile of the conditional distribution of Y given X changes as a function of X.

Logistic Regression versus Quantile Regression

It seems to me that these two procedures have totally different aims, since the first procedure (i.e., logistic regression) focuses on the q-th quantile of the unconditional distribution of Y, whereas the second procedure (i.e., quantile regression) focuses on the the q-th quantile of the conditional distribution of Y.

The unconditional distribution of Y is the 

distribution of Y values (hence it ignores any 

information about the X values). 



The conditional distribution of Y given X is the 

distribution of those Y values for which the values 

of X are the same.  

Illustrative Example

For illustration purposes, let's say Y = cholesterol and X = body weight.

Then logistic regression is modelling the odds of having a 'high' cholesterol value (i.e., greater than or equal to the q-th quantile of the observed cholesterol values) as a function of body weight, where the definition of 'high' has no relation to body weight. In other words, the marker for what constitutes a 'high' cholesterol value is independent of body weight. What changes with body weight in this model is the odds that a cholesterol value would exceed this marker.

On the other hand, quantile regression is looking at how the 'marker' cholesterol values for which q% of the subjects with the same body weight in the underlying population have a higher cholesterol value vary as a function of body weight. You can think of these cholesterol values as markers for identifying what cholesterol values are 'high' - but in this case, each marker depends on the corresponding body weight; furthermore, the markers are assumed to change in a predictable fashion as the value of X changes (e.g., the markers tend to increase as X increases).

share|cite|improve this answer

edited 35 mins ago

answered 2 hours ago

Isabella GhementIsabella Ghement

7,823422

$endgroup$

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  $begingroup$
  I agree with all that. Yet, there does seem to be a similarity - that is, both look at the qth quantile as a function of the same independent variables.
  $endgroup$
  – Peter Flom♦
  36 mins ago
  

  
  
  
  


• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  $begingroup$
  Yes, but the difference is that one method looks at the unconditional quantile (i.e., logistic regression) while the other looks at the conditional quantile (i.e., quantile regression). Those two quantiles keep track of different things.
  $endgroup$
  – Isabella Ghement
  33 mins ago
  

  
  
  
  

add a comment | 

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4

$begingroup$

For simplicity, assume you have a continuous dependent variable Y and a continuous predictor variable X.

Logistic Regression

If I understand your post correctly, your logistic regression will categorize Y into 0 and 1 based on the quantile of the (unconditional) distribution of Y. Specifically, the q-th quantile of the distribution of observed Y values will be computed and Ycat will be defined as 0 if Y is strictly less than this quantile and 1 if Y is greater than or equal to this quantile.

If the above captures your intent, then the logistic regression will model the odds of Y exceeding or being equal to the (observed) q-th quantile of the (unconditional) Y distribution as a function of X.

** Quantile Regression**

On the other hand, if you are performing a quantile regression of Y on X, you are focusing on modelling how the q-th quantile of the conditional distribution of Y given X changes as a function of X.

Logistic Regression versus Quantile Regression

It seems to me that these two procedures have totally different aims, since the first procedure (i.e., logistic regression) focuses on the q-th quantile of the unconditional distribution of Y, whereas the second procedure (i.e., quantile regression) focuses on the the q-th quantile of the conditional distribution of Y.

The unconditional distribution of Y is the 

distribution of Y values (hence it ignores any 

information about the X values). 



The conditional distribution of Y given X is the 

distribution of those Y values for which the values 

of X are the same.  

Illustrative Example

For illustration purposes, let's say Y = cholesterol and X = body weight.

Then logistic regression is modelling the odds of having a 'high' cholesterol value (i.e., greater than or equal to the q-th quantile of the observed cholesterol values) as a function of body weight, where the definition of 'high' has no relation to body weight. In other words, the marker for what constitutes a 'high' cholesterol value is independent of body weight. What changes with body weight in this model is the odds that a cholesterol value would exceed this marker.

On the other hand, quantile regression is looking at how the 'marker' cholesterol values for which q% of the subjects with the same body weight in the underlying population have a higher cholesterol value vary as a function of body weight. You can think of these cholesterol values as markers for identifying what cholesterol values are 'high' - but in this case, each marker depends on the corresponding body weight; furthermore, the markers are assumed to change in a predictable fashion as the value of X changes (e.g., the markers tend to increase as X increases).

share|cite|improve this answer

edited 35 mins ago

answered 2 hours ago

Isabella GhementIsabella Ghement

7,823422

$endgroup$

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  $begingroup$
  I agree with all that. Yet, there does seem to be a similarity - that is, both look at the qth quantile as a function of the same independent variables.
  $endgroup$
  – Peter Flom♦
  36 mins ago
  

  
  
  
  


• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  $begingroup$
  Yes, but the difference is that one method looks at the unconditional quantile (i.e., logistic regression) while the other looks at the conditional quantile (i.e., quantile regression). Those two quantiles keep track of different things.
  $endgroup$
  – Isabella Ghement
  33 mins ago
  

  
  
  
  

add a comment | 

4

$begingroup$

For simplicity, assume you have a continuous dependent variable Y and a continuous predictor variable X.

Logistic Regression

If I understand your post correctly, your logistic regression will categorize Y into 0 and 1 based on the quantile of the (unconditional) distribution of Y. Specifically, the q-th quantile of the distribution of observed Y values will be computed and Ycat will be defined as 0 if Y is strictly less than this quantile and 1 if Y is greater than or equal to this quantile.

If the above captures your intent, then the logistic regression will model the odds of Y exceeding or being equal to the (observed) q-th quantile of the (unconditional) Y distribution as a function of X.

** Quantile Regression**

On the other hand, if you are performing a quantile regression of Y on X, you are focusing on modelling how the q-th quantile of the conditional distribution of Y given X changes as a function of X.

Logistic Regression versus Quantile Regression

It seems to me that these two procedures have totally different aims, since the first procedure (i.e., logistic regression) focuses on the q-th quantile of the unconditional distribution of Y, whereas the second procedure (i.e., quantile regression) focuses on the the q-th quantile of the conditional distribution of Y.

The unconditional distribution of Y is the 

distribution of Y values (hence it ignores any 

information about the X values). 



The conditional distribution of Y given X is the 

distribution of those Y values for which the values 

of X are the same.  

Illustrative Example

For illustration purposes, let's say Y = cholesterol and X = body weight.

Then logistic regression is modelling the odds of having a 'high' cholesterol value (i.e., greater than or equal to the q-th quantile of the observed cholesterol values) as a function of body weight, where the definition of 'high' has no relation to body weight. In other words, the marker for what constitutes a 'high' cholesterol value is independent of body weight. What changes with body weight in this model is the odds that a cholesterol value would exceed this marker.

On the other hand, quantile regression is looking at how the 'marker' cholesterol values for which q% of the subjects with the same body weight in the underlying population have a higher cholesterol value vary as a function of body weight. You can think of these cholesterol values as markers for identifying what cholesterol values are 'high' - but in this case, each marker depends on the corresponding body weight; furthermore, the markers are assumed to change in a predictable fashion as the value of X changes (e.g., the markers tend to increase as X increases).

share|cite|improve this answer

edited 35 mins ago

answered 2 hours ago

Isabella GhementIsabella Ghement

7,823422

$endgroup$

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  $begingroup$
  I agree with all that. Yet, there does seem to be a similarity - that is, both look at the qth quantile as a function of the same independent variables.
  $endgroup$
  – Peter Flom♦
  36 mins ago
  

  
  
  
  


• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  $begingroup$
  Yes, but the difference is that one method looks at the unconditional quantile (i.e., logistic regression) while the other looks at the conditional quantile (i.e., quantile regression). Those two quantiles keep track of different things.
  $endgroup$
  – Isabella Ghement
  33 mins ago
  

  
  
  
  

add a comment | 

$begingroup$

For simplicity, assume you have a continuous dependent variable Y and a continuous predictor variable X.

Logistic Regression

If I understand your post correctly, your logistic regression will categorize Y into 0 and 1 based on the quantile of the (unconditional) distribution of Y. Specifically, the q-th quantile of the distribution of observed Y values will be computed and Ycat will be defined as 0 if Y is strictly less than this quantile and 1 if Y is greater than or equal to this quantile.

If the above captures your intent, then the logistic regression will model the odds of Y exceeding or being equal to the (observed) q-th quantile of the (unconditional) Y distribution as a function of X.

** Quantile Regression**

On the other hand, if you are performing a quantile regression of Y on X, you are focusing on modelling how the q-th quantile of the conditional distribution of Y given X changes as a function of X.

Logistic Regression versus Quantile Regression

It seems to me that these two procedures have totally different aims, since the first procedure (i.e., logistic regression) focuses on the q-th quantile of the unconditional distribution of Y, whereas the second procedure (i.e., quantile regression) focuses on the the q-th quantile of the conditional distribution of Y.

The unconditional distribution of Y is the 

distribution of Y values (hence it ignores any 

information about the X values). 



The conditional distribution of Y given X is the 

distribution of those Y values for which the values 

of X are the same.  

Illustrative Example

For illustration purposes, let's say Y = cholesterol and X = body weight.

Then logistic regression is modelling the odds of having a 'high' cholesterol value (i.e., greater than or equal to the q-th quantile of the observed cholesterol values) as a function of body weight, where the definition of 'high' has no relation to body weight. In other words, the marker for what constitutes a 'high' cholesterol value is independent of body weight. What changes with body weight in this model is the odds that a cholesterol value would exceed this marker.

On the other hand, quantile regression is looking at how the 'marker' cholesterol values for which q% of the subjects with the same body weight in the underlying population have a higher cholesterol value vary as a function of body weight. You can think of these cholesterol values as markers for identifying what cholesterol values are 'high' - but in this case, each marker depends on the corresponding body weight; furthermore, the markers are assumed to change in a predictable fashion as the value of X changes (e.g., the markers tend to increase as X increases).

share|cite|improve this answer

edited 35 mins ago

answered 2 hours ago

Isabella GhementIsabella Ghement

7,823422

$endgroup$

The unconditional distribution of Y is the 

distribution of Y values (hence it ignores any 

information about the X values). 



The conditional distribution of Y given X is the 

distribution of those Y values for which the values 

of X are the same.  

share|cite|improve this answer

edited 35 mins ago

answered 2 hours ago

Isabella GhementIsabella Ghement

7,823422

answered 2 hours ago

Isabella GhementIsabella Ghement

7,823422

answered 2 hours ago

Isabella GhementIsabella Ghement

7,823422