How is the half-normal distribution associated with the c.d.f formula derived?

In summary, the author presents a formula for the c.d.f. for the half-normal distribution as:G(x)^{2r-2}=\left(\frac{2}{\pi } \right)^{r-1}\left\{ \int_{0}^{x}\exp \left [ -\frac{1}{2} \left (w ^{2}\right )\right ]dw \right\}.
  • #1
Zoran
4
0
Hi: I am reading an article that deals with the distribution function associated with the half-normal distribution. The author presents a formula for the c.d.f. as:

[tex] \left [ G\left ( x \right ) \right ]^{2r-2}=\left ( \frac{2}{\pi } \right )^{r-1}\left \{ \int_{0}^{x}\exp \left [ -\frac{1}{2} \left (w ^{2}\right )\right ]dw \right \}^{2r-2} [/tex]

Note that r=2,3,...

The expression above is also equal to:

[tex] \left [ G\left ( x \right ) \right ]^{2r-2}=\left ( \frac{2}{\pi } \right )^{r-1}\left \{ \int_{0}^{x}\int_{0}^{x} \exp \left [- \frac{1}{2}\left ( x_{1}^{2}+x_{2}^{2} \right ) \right ]dx_{1}dx_{2}\right \}^{r-1} [/tex]

which I have no problem with. The author then states that the second equation above is also equal to:

[tex] \left [ G\left ( x \right ) \right ]^{2r-2}=\left ( \frac{4}{\pi } \right )^{r-1}\left \{ \int_{0}^{\frac{\pi }{4}}\left [ 1-\exp \left ( -\frac{1}{2}x^{2}\sec ^{2}\theta \right ) \right ]d\theta \right \}^{r-1} [/tex]

Can someone explain to me how the author gets from the second equation to the third (or last) equation above.


Thanks in advance.
 
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  • #2
Zoran said:
Hi: I am reading an article that deals with the distribution function associated with the half-normal distribution. The author presents a formula for the c.d.f. as:

[tex] \left [ G\left ( x \right ) \right ]^{2r-2}=\left ( \frac{2}{\pi } \right )^{r-1}\left \{ \int_{0}^{x}\exp \left [ -\frac{1}{2} \left (w ^{2}\right )\right ]dw \right \}^{2r-2} [/tex]

Note that r=2,3,...

The expression above is also equal to:

[tex] \left [ G\left ( x \right ) \right ]^{2r-2}=\left ( \frac{2}{\pi } \right )^{r-1}\left \{ \int_{0}^{x}\int_{0}^{x} \exp \left [- \frac{1}{2}\left ( x_{1}^{2}+x_{2}^{2} \right ) \right ]dx_{1}dx_{2}\right \}^{r-1} [/tex]

which I have no problem with. The author then states that the second equation above is also equal to:

[tex] \left [ G\left ( x \right ) \right ]^{2r-2}=\left ( \frac{4}{\pi } \right )^{r-1}\left \{ \int_{0}^{\frac{\pi }{4}}\left [ 1-\exp \left ( -\frac{1}{2}x^{2}\sec ^{2}\theta \right ) \right ]d\theta \right \}^{r-1} [/tex]

Can someone explain to me how the author gets from the second equation to the third (or last) equation above.

Since an angle has been introduced in the final result, I would try to express the integral using polar coordinates in the x1,x2 plane.
 
  • #3
You basically want to show this:

[tex] \int_{0}^{x}\int_{0}^{x} \exp \left [- \frac{1}{2}\left ( x_{1}^{2}+x_{2}^{2} \right ) \right ]dx_{1}dx_{2} = 2 \int_{0}^{\frac{\pi }{4}}\left [ 1-\exp \left ( -\frac{1}{2}x^{2}\sec ^{2}\theta \right ) \right ]d\theta [/tex]

Since you are integrating over a rectangle from (0,0) to (x,x) here, you can't really use spherical coordinates.

You can, however, use an angle theta=0..pi/4 and one cartesian coordinate, i.e. x_1 to describe half of your rectangle. The transformation should look kinda like

[tex] x_1=x_1 [/tex]
[tex] x_2 = x_1 \tan \theta [/tex]

For the argument inside the exponential function, you will get
[tex] x_1^2 + x_2^2 = x_1 (\tan^2\theta + 1) = x_1^2 \sec^2 \theta [/tex]
For your Jacobian, you will get:
[tex] x_1 (\tan\theta)' = x_1 \sec^2\theta [/tex]
Once you do the x_1-Integration, the theta-Part of the Jacobian will go away.
 
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  • #4
susskind_leon said:
You basically want to show this:

[tex] \int_{0}^{x}\int_{0}^{x} \exp \left [- \frac{1}{2}\left ( x_{1}^{2}+x_{2}^{2} \right ) \right ]dx_{1}dx_{2} = 2 \int_{0}^{\frac{\pi }{4}}\left [ 1-\exp \left ( -\frac{1}{2}x^{2}\sec ^{2}\theta \right ) \right ]d\theta [/tex]

Since you are integrating over a rectangle from (0,0) to (x,x) here, you can't really use spherical coordinates.

You can, however, use an angle theta=0..pi/4 and one cartesian coordinate, i.e. x_1 to describe half of your rectangle. The transformation should look kinda like

[tex] x_1=x_1 [/tex]
[tex] x_2 = x_1 \tan \theta [/tex]

For the argument inside the exponential function, you will get
[tex] x_1^2 + x_2^2 = x_1 (\tan^2\theta + 1) = x_1^2 \sec^2 \theta [/tex]
For your Jacobian, you will get:
[tex] x_1 (\tan\theta)' = x_1 \sec^2\theta [/tex]
Once you do the x_1-Integration, the theta-Part of the Jacobian will go away.


Okay, thanks.

Note that I think you meant to write:

[tex] x_1^2 + x_2^2 = x_1^2 (\tan^2\theta + 1) = x_1^2 \sec^2 \theta [/tex]
 
Last edited:
  • #5
Yup, of course, sry ;)
 

FAQ: How is the half-normal distribution associated with the c.d.f formula derived?

1. What is a half-normal distribution?

A half-normal distribution is a type of probability distribution where the data is skewed to the right and has a long tail on the positive side. It is a variation of the normal distribution, but only accounts for data on one side of the mean.

2. How is a half-normal distribution different from a normal distribution?

A normal distribution is symmetric, with data evenly distributed around the mean. A half-normal distribution, on the other hand, has a long tail on the positive side and is skewed to the right.

3. What is the purpose of using a half-normal distribution?

A half-normal distribution is often used in scientific research to model data that is skewed to the right. It can also be used in regression analysis to assess the fit of a model and identify outliers.

4. How is a half-normal distribution related to the chi-square distribution?

A half-normal distribution is a special case of the chi-square distribution, specifically when the degrees of freedom are equal to 2. This means that the squared values of a half-normal distribution will follow a chi-square distribution with 2 degrees of freedom.

5. How do you calculate the mean and standard deviation of a half-normal distribution?

The mean of a half-normal distribution can be calculated by taking the square root of 2 divided by pi, which is approximately 0.798. The standard deviation can be calculated by taking the square root of 1 minus the mean squared, which is approximately 0.603.

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