Relationship between dist. of x and dist. of 1/x?

In summary, the conversation discussed the relationship between the distribution of stiffness and compliance values in tissue cell mechanics. It was noted that there is no universal statement that can be made about the distribution of 1/x when x is distributed in a certain way. However, it was suggested that a transformation approach can be used to determine the distribution of 1/x. Ultimately, the type of distribution for x and 1/x cannot be simplified.
  • #1
Mapes
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I've been working recently in the area of tissue cell mechanics; specifically, I'm measuring mechanical stiffness (or compliance, the reciprocal of stiffness) and considering its possible underlying distribution.

I was wondering about the following: If the distribution of stiffness measurements is approximately Gaussian (or lognormal, or gamma distributed, etc.), then what can we say about the distribution of the corresponding compliance (= 1/stiffness) values? More generally, if [itex]x[/itex] is distributed in a certain way, what about [itex]1/x[/itex]? Is there a simple relationship?
 
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  • #2
No - there is no universal statement that can be made. Each case needs to be considered on its own.
(The mathematical ideas behind studying the distributions is the same in each case, but unless I'm totally off that wasn't the point of your inquiry.)
 
  • #3
Mapes said:
More generally, if [itex]x[/itex] is distributed in a certain way, what about [itex]1/x[/itex]? Is there a simple relationship?

There is a relationship, how simple depends on the details of your example.

Given a probability distribution f(x), we seek the distribution g(y) where y is a function of x. A simple probability conservation argument tells us that

f(x) |dx| = g(y) |dy|​

so that

g(y) = f(x) / |dy/dx|​

Take y = 1/x, and f(x) is whatever you think, you can get g(y).


EDIT:
Continuing the example for y = 1/x

Since |dy/dx| = 1/x2 = y2, we have

g(y) = f(x) / y2

And, of course, you would substitute 1/y for x in the expression for f(x).
 
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  • #4
The transformation approach is correct (modulo being careful around x = 0); my intention was to say there is nothing simple to say about the type of distribution for X and the type for 1/X (normal to normal, t to t, etc).
 
  • #5
True :smile:
 

1. What is the relationship between the distribution of x and the distribution of 1/x?

The distribution of 1/x is the inverse of the distribution of x. This means that the values of 1/x will be the reciprocal of the values of x. For example, if x follows a normal distribution with a mean of 10 and a standard deviation of 2, then 1/x will follow a normal distribution with a mean of 0.1 and a standard deviation of 0.2.

2. How does the shape of the distribution of x affect the distribution of 1/x?

The shape of the distribution of x will affect the shape of the distribution of 1/x. If the distribution of x is skewed, then the distribution of 1/x will also be skewed in the opposite direction. If the distribution of x is symmetric, then the distribution of 1/x will also be symmetric. However, the scale of the distribution of 1/x will be different from the scale of the distribution of x.

3. Can the distribution of 1/x be used to make predictions about the distribution of x?

No, the distribution of 1/x cannot be used to make predictions about the distribution of x. While the two distributions are related, they are not interchangeable. The distribution of 1/x represents the inverse values of x and cannot accurately predict the distribution of x.

4. How does the mean and standard deviation of x affect the mean and standard deviation of 1/x?

The mean and standard deviation of 1/x will be the reciprocal of the mean and standard deviation of x. This means that if the mean of x is 5, then the mean of 1/x will be 1/5. Similarly, if the standard deviation of x is 2, then the standard deviation of 1/x will be 1/2.

5. Can the distribution of 1/x be used to transform a non-normal distribution into a normal distribution?

No, the distribution of 1/x cannot be used to transform a non-normal distribution into a normal distribution. While it can change the shape of the distribution, it cannot change the underlying data. It is important to use appropriate statistical techniques to transform data into a normal distribution, rather than relying on the distribution of 1/x.

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