Polynomial transformation of random variable

In summary, the conversation discusses finding the distribution of a function of a random variable with a known distribution, specifically f(X) = X^2 + X. The standard methods such as the transformation theorem and Laplace transformation do not seem to work, but there is a formula for functions of random variables. However, the function in question may not be easily invertible, so solving the inequality may be necessary.
  • #1
raynard
9
0

Homework Statement



Given a random variable X with a known distribution (e.g. a beta distribution), find the distribution of
f(X) = X^2 + X

The Attempt at a Solution



I've tried the normal approaches: the standard transformation theorem; conditioning on X; Laplace transformation, etc. They don't seem to work. Any hints?
 
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  • #3
Thanks for the reply!

I tried the standard method you described, but I see no easy way to find the inverse of

f: x -> x^2 + x
 
  • #4
Yup, I don't think it's very pretty...
I have to say, I'm pretty rusty when it comes to probability. I remember the best ways to solve this type of questions is to write Y = X2 + X, and then:
P(Y < y) = P (X2 + X < y) = P(X2 + X - y < 0 ) = ...

Then you'd have to solve this inequality and proceed from there...
But maybe there are shortcuts. I just responded cause I saw no one else did :-)
 

FAQ: Polynomial transformation of random variable

What is a polynomial transformation of a random variable?

A polynomial transformation of a random variable is a process of creating a new random variable by performing mathematical operations, such as addition, subtraction, multiplication, and division, on an existing random variable. This allows for the manipulation and analysis of the underlying probability distribution of the original random variable.

What is the purpose of polynomial transformation of a random variable?

The purpose of polynomial transformation of a random variable is to simplify the analysis of a complex probability distribution and to make it easier to apply statistical methods and techniques. It also allows for the transformation of non-normal distributions into approximately normal distributions, which can be useful in certain statistical analyses.

What are some common examples of polynomial transformations of random variables?

Some common examples of polynomial transformations of random variables include squaring, taking the square root, raising to a power, and taking logarithms. These transformations are commonly used in statistics and data analysis to simplify the underlying probability distribution and make it easier to apply statistical methods.

What are the key properties of polynomial transformations of random variables?

The key properties of polynomial transformations of random variables include preserving the mean and variance of the original random variable, as well as preserving the order of the values. This means that the relative positions of the data points will remain the same after the transformation.

What are some potential pitfalls of polynomial transformations of random variables?

One potential pitfall of polynomial transformations of random variables is the risk of creating a biased or incorrect analysis if the transformation is not appropriate for the data. Additionally, polynomial transformations can sometimes result in non-intuitive or difficult to interpret results, making it important to carefully consider the choice of transformation and its implications on the data.

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