Finding the Gamma Distribution of X1+...+Xn

In summary, the problem deals with finding the density function of the sum of independent, identically distributed random variables with exponential distribution of parameter λ, which is known as the gamma distribution of parameters n and λ. The approach is to use induction and begin by calculating the convolution integral of the exponential distribution. The exponential distribution has a zero domain for negative arguments, so the limits must be taken into account when setting up the integral. After solving the integral, the form of the function can be used to proceed with the induction step.
  • #1
glacier302
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Homework Statement



Let X1,...,Xn be independent, identically distributed random variables with exponential distribution of parameter λ. Find the density function of S = X1+...+Xn. (This distribution is called the gamma distribution of parameters n and λ). Hint: Proceed by induction.


Homework Equations



The probability density function of each of the Xi is f(x) = λe^(-λx).

The probability density function of the sum of two independent random variables is the convolution of their density functions. So if the density function of X is f(x) and the density function of Y is g(x), then the density function of X+Y is ∫f(T)g(x-T)dT (integral from -∞ to ∞).


The Attempt at a Solution



At first I tried computing the characteristic function of X1+...+Xn, which is equal to the characteristic function of X1 raised to the nth power since the Xi are independent and identically distributed. But this didn't look like the characteristic function of any probability distribution that I know, so that was a dead end.

We're told to proceed by induction, but I'm not sure how to do that with density functions.

Any help would be much appreciated! : )
 
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  • #2


I would begin by actually calculating that convolution integral. So letting f(x) represent your exponential distribution λe-λx you want to calculate

[tex]\int_{-\infty}^\infty \int_{-\infty}^\infty f(t)f(x-t)\, dt[/tex]

But you need to take care about the limits. Remember the exponential distribution is zero whenever its argument is negative. So your first problem is to put in the exponential functions with the correct limits for the non-zero domain. Then just do the integral and look at its form. Then you will be ready to try the induction step.
 

What is the gamma distribution?

The gamma distribution is a continuous probability distribution that is commonly used to model the times between events, such as the time between phone calls or the time between accidents.

How is the gamma distribution related to X1+...+Xn?

The gamma distribution of X1+...+Xn is a way to model the sum of independent and identically distributed random variables. It represents the total time or occurrences of an event that is made up of n smaller events.

What are the main properties of the gamma distribution?

The gamma distribution has two parameters, shape (α) and scale (β), which determine its shape and location. It is a skewed distribution, with a shape parameter that controls the degree of skewness. It is also a continuous distribution, meaning that it can take on any value within a certain range.

How is the gamma distribution calculated?

The gamma distribution is typically calculated using the gamma function, which is a special function in mathematics that is used to calculate the area under the curve of the gamma distribution. The formula for the gamma distribution is: f(x) = (x^(α-1) * e^(-x/β)) / (β^(α) * Γ(α)), where Γ(α) is the gamma function.

What are some common applications of the gamma distribution?

The gamma distribution is commonly used in fields such as engineering, economics, and physics to model the times between events or the occurrence of events. It is also used in survival analysis, where it is used to model the time to failure of a system or component. Additionally, it is used in statistical modeling and data analysis to fit data and make predictions.

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