Random Variable Transformation

In summary, the conversation discusses the formation of a random variable Y by transforming another random variable X using a transforming function g(.). The focus is on how to specify g(.) given the Probability Density Function (PDF) of both RVs. It is suggested to use the Cumulative Distribution Functions (CDFs) and assume g is one-to-one and monotonic.
  • #1
EngWiPy
1,368
61
Hello,

Suppose that a random variable Y is formed by transforming another random variable X by using the tranforming function g(.). That is:

[tex]Y=\,g(X)[/tex]

Now, given that we have the Probabililty Density Function (PDF) of both RVs: [tex]f_Y(y)\mbox{ and }f_X(x)[/tex], how can we specify g(.)? I didn't give an exact example because I just need to know the procedure.

Thanks in advance
 
Physics news on Phys.org
  • #2
S_David said:
Hello,

Suppose that a random variable Y is formed by transforming another random variable X by using the tranforming function g(.). That is:

[tex]Y=\,g(X)[/tex]

Now, given that we have the Probabililty Density Function (PDF) of both RVs: [tex]f_Y(y)\mbox{ and }f_X(x)[/tex], how can we specify g(.)? I didn't give an exact example because I just need to know the procedure.

Thanks in advance

It'll be a bit easier to use the CDFs and assuming g is one-to-one & monotonic:

F_X(x) = Prob(X<=x) etc.
 
  • #3
bpet said:
It'll be a bit easier to use the CDFs and assuming g is one-to-one & monotonic:

F_X(x) = Prob(X<=x) etc.

Ok, then?
 

Related to Random Variable Transformation

What is a random variable transformation?

A random variable transformation is a mathematical process that maps the values of one random variable to the values of another random variable. It is used in statistics and probability to transform data into a different form, often to make it easier to analyze or interpret.

Why is random variable transformation important?

Random variable transformation is important because it allows us to manipulate or change the characteristics of a random variable, such as its distribution, to better suit our needs for analysis. It also helps us to understand the relationship between different variables in a dataset.

What are some common methods of random variable transformation?

Some common methods of random variable transformation include logarithmic transformation, exponential transformation, and power transformation. These methods are often used to transform data that is skewed or has a non-normal distribution into a more normal distribution.

What are the benefits of using random variable transformation?

The benefits of using random variable transformation include making data easier to interpret and analyze, improving the accuracy of statistical models, and allowing for the use of certain statistical tests that require data to be normally distributed.

Are there any limitations to random variable transformation?

Yes, there are some limitations to random variable transformation. It may not always be possible to transform data into a normal distribution, and the transformation process can sometimes introduce errors or distortions in the data. Additionally, the interpretation of the transformed data may not always be straightforward.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
3
Views
853
  • Set Theory, Logic, Probability, Statistics
Replies
7
Views
559
  • Set Theory, Logic, Probability, Statistics
Replies
30
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
10
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
9
Views
645
  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
6
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
4
Views
2K
Back
Top