Integrating normal from 0 to Inf and finding the third moment

In summary, the conversation was about solving the integral ∫ x^3 f(x) dx from 0 to ∞, where f(x) is the pdf of a normal distribution with mean (-σ^2/b) and variance σ^2. The suggested method was to use integration by parts, but it was noted that there is no "nice" antiderivative for this integral, meaning an antiderivative in a closed form that can be calculated without using numerical methods or tables.
  • #1
Hejdun
25
0
This is (perhaps) a tricky question regarding the moment of normal distribution.
Let f(x) be the pdf of normal distribution with mean (-σ^2/b) and variance σ^2, where b is just a constant. The goal is to solve the integral

∫ x^3 f(x) dx

integrating from 0 to ∞.

I am stuck. Any suggestions?
 
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  • #2
Use integration by parts to express the problem as one in lower powers of X. The expressions involving the lower powers will have something to do with lower order moments.

If it is a normal distribution, aren't you integrating from [itex] -\infty [/itex] to [itex] \infty [/itex]?
 
  • #3
Stephen Tashi said:
Use integration by parts to express the problem as one in lower powers of X. The expressions involving the lower powers will have something to do with lower order moments.

If it is a normal distribution, aren't you integrating from [itex] -\infty [/itex] to [itex] \infty [/itex]?


I will try integration by parts, but I am not sure if that would solve it.

No, unfortunately it is from 0 to ∞. Otherwise it would have been a standard result.
 
  • #4
Since the exponent will contain multiples of x^2 and x, h'about trying a substitution like:

x^2=u , then x^3=u^(3/2) , etc.
 
  • #5
It seems that there is no analyc answer to this integral since integration will result in an error function. But thanks anyway!
 
  • #6
But the Error function is analytic! It is an entire function.
 
  • #7
Herick said:
But the Error function is analytic! It is an entire function.

I think by analytic Hedjun means that the function has an antiderivative in a

"nice closed form" , not in the complex-analytic sense.

Notice that in this case f is assumed real-valued.
 
Last edited:
  • #8
Well, is it [itex]e^x[/itex] a closed form? If by closed form he means a convergent infinite series, then Err(x) is also 'closed form'. Right?
 
  • #9
Herick said:
Well, is it [itex]e^x[/itex] a closed form? If by closed form he means a convergent infinite series, then Err(x) is also 'closed form'. Right?

I assume s/he , means a 'nice' antiderivative ( a combination of sums, products of

known/common functions ) F that would allow you to calculate the area between any two

x,y , without having to use numerical methods , or tables. AFAIK there isn't any one.

Do you know of one? I assumed, since tables are used for the normal density, that

there isn't any "nice-enough" antiderivative. But maybe the OP can tell us what s/he

was looking for.
 
  • #10
Bacle2 said:
I assume s/he , means a 'nice' antiderivative ( a combination of sums, products of

known/common functions ) F that would allow you to calculate the area between any two

x,y , without having to use numerical methods , or tables. AFAIK there isn't any one.

Do you know of one? I assumed, since tables are used for the normal density, that

there isn't any "nice-enough" antiderivative. But maybe the OP can tell us what s/he

was looking for.

Yes, it is this that I was looking for, which I believe does not exist for the integral that started this thread. Thanks for your replies.
 

What is the purpose of integrating normal from 0 to Inf and finding the third moment?

The purpose of integrating normal from 0 to Inf and finding the third moment is to characterize the distribution of a continuous random variable. It allows us to calculate the mean, variance, and other moments of the distribution, which can provide important insights into the behavior of the variable.

How do you integrate normal from 0 to Inf and find the third moment?

To integrate normal from 0 to Inf and find the third moment, you can use the formula for the moment generating function (MGF) of the normal distribution. This involves taking the integral of the MGF from 0 to Inf and then differentiating three times with respect to the parameter of interest. Alternatively, you can use numerical methods or statistical software to calculate the third moment.

What is the mathematical significance of the third moment?

The third moment, also known as the skewness, is a measure of the symmetry of a distribution. A positive third moment indicates that the distribution is skewed to the right, while a negative third moment indicates a skew to the left. A third moment of 0 indicates a symmetrical distribution.

What does a high third moment indicate about the shape of the distribution?

A high third moment indicates that the distribution is highly asymmetric, with a long tail on one side. This can be seen in the shape of the distribution curve, with a longer tail on one side compared to the other. It can also indicate that the distribution has a higher probability of extreme values, as the tail of the distribution is stretched out.

How can the third moment be used in real-world applications?

The third moment, along with other moments, can be used to compare and analyze different distributions in real-world applications. It can provide insights into the shape and behavior of a continuous random variable, which can be useful in fields such as finance, economics, and engineering. It can also be used to identify outliers or anomalies in data sets, which can help in detecting patterns or making predictions.

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