Transform Random Var CDF to Standard Normal: F(x)=1-exp(-sqrt x)

In summary, the transformation of a random variable CDF to a standard normal distribution is given by the formula y=g(x)=Phi^(-1)(F(x)) where y is the transformed variable and x is the original variable with a CDF of F(x)=1-exp(-x). This formula cannot be simplified further as it involves the inverse of the standard normal CDF function which cannot be expressed in terms of standard functions.
  • #1
rvkhatri
3
0
How to transform a random variable CDF to a standard normal
Given F(x) = 1- exp (-sqrt x), for x greater that 0

Thanks.
 
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  • #2
rvkhatri said:
How to transform a random variable CDF to a standard normal
Given F(x) = 1- exp (-sqrt x), for x greater that 0

Thanks.

Welcome to MHB, rvkhatri! :)

What do you mean by "standard normal"?

Do you mean the PDF?
Or perhaps an equivalent normal distribution?
 
  • #3
I like Serena said:
Welcome to MHB, rvkhatri! :)

What do you mean by "standard normal"?

Do you mean the PDF?
Or perhaps an equivalent normal distribution?

I meant standard normal distribution i.e. mean = 0, sigma = 1

My class notes say,
if F(x) = 1- exp (-x), there could be one-to-one transformation to a standard normal distribution. But I am not able to get a start on this.
 
  • #4
rvkhatri said:
I meant standard normal distribution i.e. mean = 0, sigma = 1

My class notes say,
if F(x) = 1- exp (-x), there could be one-to-one transformation to a standard normal distribution. But I am not able to get a start on this.

Suppose the transformation is given by $y=g(x)$.
That is, if X is distributed according to your exponential F(X), then we will have g(X) ~ N(0,1).

Let $\Phi(y)$ be the CDF of the standard normal distribution.

Then the transformation $g$ needs to be such that the standard normal cumulative probability up to y must be the same as the exponential cumulative probability up to x.
As a formula:
$$\Phi(y) = F(x)$$

In other words:
$$y = g(x) = \Phi^{-1}(F(x))$$
 
  • #5
I like Serena said:
Suppose the transformation is given by $y=g(x)$.
That is, if X is distributed according to your exponential F(X), then we will have g(X) ~ N(0,1).

Let $\Phi(y)$ be the CDF of the standard normal distribution.

Then the transformation $g$ needs to be such that the standard normal cumulative probability up to y must be the same as the exponential cumulative probability up to x.
As a formula:
$$\Phi(y) = F(x)$$

In other words:
$$y = g(x) = \Phi^{-1}(F(x))$$

My class note gives me exactly this formula for trasformation.

Now how do we get value of y in terms of x.
 
  • #6
rvkhatri said:
My class note gives me exactly this formula for trasformation.

Now how do we get value of y in terms of x.

We already have.

You're probably thinking of rewriting it into an expression using only standard functions.
But I'm afraid we can't.
The function $\Phi(x)$ cannot be expressed as a finite combination of standard functions (this has been proven mathematically).
As a result $\Phi^{-1}(1-e^{-x})$ cannot be expressed in such a form.

The expression we have is as simple as it gets.
 

FAQ: Transform Random Var CDF to Standard Normal: F(x)=1-exp(-sqrt x)

How is the CDF of a random variable transformed into a standard normal distribution?

The CDF of a random variable can be transformed into a standard normal distribution by using the inverse of the cumulative distribution function (CDF) of a standard normal distribution. This transformation is also known as the Box-Cox transformation.

What is the purpose of transforming a CDF to a standard normal distribution?

The purpose of transforming a CDF to a standard normal distribution is to simplify and standardize the data. This allows for easier comparison and analysis of different sets of data, as well as making it easier to apply statistical tests and models.

How is the transformation formula for F(x)=1-exp(-sqrt x) derived?

The transformation formula for F(x)=1-exp(-sqrt x) is derived using the Box-Cox transformation method, which involves taking the natural log of the original data and then applying a power transformation. In this case, the power transformation is taking the square root of the original data.

What are the advantages of using a standard normal distribution?

Using a standard normal distribution has several advantages, including allowing for easier calculation and interpretation of statistical measures such as mean, standard deviation, and correlation. It also allows for the use of many common statistical tests and models, such as the t-test and ANOVA.

Can any CDF be transformed into a standard normal distribution?

No, not all CDFs can be transformed into a standard normal distribution. The CDF must have a known inverse function and the data must be continuous and normally distributed for the transformation to be valid. Additionally, some datasets may require a different type of transformation to achieve normality.

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