Understanding Notation for Integration in Tempered Alpha Stable Distributions

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In summary, the conversation discusses the notation \int_{R^d} and its meaning in relation to a journal article about tempered alpha stable distributions. The conversation also mentions the Gamma function and a theorem about the Levy measure of tempered alpha stable distributions. The main question is how to determine the function R(dx) in the mentioned theorem.
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cernlife
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I'm reading this journal article and they keep on using the notation

[tex]\int_{R^d}[/tex]

What does this mean, just say d=1, does it then mean

[tex]\int_0^{\infty}[/tex]

or

[tex]\int_{-\infty}^{\infty}[/tex]

any help much appreciated.
 
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  • #2
It means the second one, but you have to take into account the support of whatever you're integrating, but if the support is over all of R then the integral will be over all of R as well.
 
  • #3
I am trying to find a function R(dx) in a paper by Rosinski "Tempering Stable Processes" which has the following theorem

Theorem 2.3. The Levy measure M of a tempered alpha stable distribution can be written in the form

[tex]M(A) = \int_{R^d}\int_0^{\infty} \textbf{I}_A(tx)t^{-\alpha-1}e^{-t}dtR(dx)[/tex]

where I_A(tx) I assume is the indicator funtion, i.e. tx is defined on the interval A

now I'm using definition of the Gamma function kernal to say

[tex]\int_0^{\infty} t^{-\alpha-1}e^{-t}dt = \Gamma(-\alpha)[/tex]


I know the Levy measure M, and so putting that in for M, I then had

[tex]2^{\alpha}\delta\frac{\alpha}{\Gamma(1-\alpha)}x^{-1-\alpha}e^{-0.5\gamma^{1/\alpha}x} = \Gamma(-\alpha)\int_{R^d}R(dx)[/tex]

So I need to work out what the function R(dx) is, which is my problem. I was thinking I could just differentiate both sides which would get the integral out of the right hand side and then I could easily rearrange to find R(dx). However I am not sure about this method as I have this integral over the range R^d.

How do I work out what R(dx) is?
 

FAQ: Understanding Notation for Integration in Tempered Alpha Stable Distributions

What is integration over the reals?

Integration over the reals is a mathematical concept that involves finding the area under a curve on the real number line. It is a fundamental tool in calculus and is used to solve a variety of problems in physics, engineering, and economics.

Why is integration over the reals important?

Integration over the reals allows us to calculate the total value or quantity of a continuously changing quantity. This is useful in many real-world scenarios, such as determining the distance traveled by a moving object, the total sales of a company over a period of time, or the total energy produced by a power plant.

What are the different methods of integration over the reals?

The two main methods of integration over the reals are the Riemann integral and the Lebesgue integral. The Riemann integral is based on dividing the area under the curve into smaller rectangles, while the Lebesgue integral is based on measuring the size of the sets where the function is above or below a certain value.

What are the applications of integration over the reals?

Integration over the reals has numerous applications in mathematics, science, and engineering. It is used to solve problems in physics, such as calculating the work done by a force, and in economics, such as determining the total profit of a company. It is also used in probability and statistics to calculate probabilities and expected values.

What are the limitations of integration over the reals?

Integration over the reals has some limitations, such as the fact that it cannot be used to find the area under a curve that has an infinite number of oscillations or infinitely steep slopes. It also cannot be used to find the area under a curve that has an infinite value at a point. In these cases, more advanced techniques, such as improper integrals, must be used.

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