Finding the Density of X with Exponential and Random Variables

In summary, the density of X is found by taking the joint probability of X being less than or equal to x and W being either 1 or -1, and multiplying it by 1/2 for each case. This results in the density function:f_{x}(x)=(\lambda/2)e^{-(\lambda)\left|x\right|}where x > 0, andf_{x}(x)=(\lambda/2)e^{(\lambda)\left|x\right|}where x < 0.
  • #1
gajohnson
73
0

Homework Statement



Let T be an exponential random variable with parameter λ; let W be a random
variable independent of T , which is ±1 with probability 1/2 each; and let X =
WT. Show that the density of X is:

[itex]f_{x}(x)=(\lambda/2)e^{-(\lambda)\left|x\right|}[/itex]

Homework Equations



Density function for exponential distribution:

[itex](\lambda)e^{-(\lambda)x}[/itex]

and the CDF for the exponential distribution:

[itex]1-e^{-(\lambda)x}[/itex]

The Attempt at a Solution



Well I wanted to break the equation into:

[itex]P[X≤x, W=1](1/2) + P[X≤x, W=-1](1/2)[/itex]

and then differentiate the result to find the density function for X.

Will this work? If not, is there another approach someone can suggest. If so, a little help with the computation would be helpful because I've been having a tough time getting anywhere with it (I know it shouldn't be hard, that's why I'm stumped). Thanks!
 
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  • #2
gajohnson said:
Let T be an exponential random variable with parameter λ; let W be a random
variable independent of T , which is ±1 with probability 1 each;
Half each?
and let X = 2WT. Show that the density of X is:

[itex]f_{x}(x)=(\lambda/2)e^{-\lambda}[/itex]
Where is x on the RHS?
 
  • #3
Ok, problem statement and relevant equations are revised. Not sure how that happened, sorry! Hope it makes more sense now...
 
  • #4
gajohnson said:
[itex]P[X≤x, W=1](1/2) + P[X≤x, W=-1](1/2)[/itex]
Nearly right. You don't need the divisions by 2.
You then need to consider what the above reduces to separately for x > 0, < 0.
 
  • #5
haruspex said:
Nearly right. You don't need the divisions by 2.
You then need to consider what the above reduces to separately for x > 0, < 0.

Good to know I'm on the right track. The issue I'm having now is proceeding with the calculation.

I get led to this:
[itex](1-e^{-(\lambda)x}) + (-1+e^{-(\lambda)x})[/itex] = 0, which is clearly incorrect.
 
  • #6
gajohnson said:
Good to know I'm on the right track. The issue I'm having now is proceeding with the calculation.

I get led to this:
[itex](1-e^{-(\lambda)x}) + (-1+e^{-(\lambda)x})[/itex] = 0, which is clearly incorrect.
As I said, you need to consider x > 0 and x < 0 separately. For x < 0, what is P[X≤x,W=1]? Remember that T does not take negative values.
 
  • #7
haruspex said:
As I said, you need to consider x > 0 and x < 0 separately. For x < 0, what is P[X≤x,W=1]? Remember that T does not take negative values.

I believe I got it, thanks for bearing with me!
 

Related to Finding the Density of X with Exponential and Random Variables

What is "Double Exponential Density"?

"Double Exponential Density" is a probability distribution function that is used to model continuous random variables. It is also known as the Laplace distribution or the exponential power distribution.

What are the characteristics of the Double Exponential Density?

The Double Exponential Density has a symmetric bell-shaped curve, with a peak at the mean. It has a heavy tail, which means it has a higher probability of extreme values compared to other distributions. It is also characterized by two parameters: location and scale.

What is the difference between the Double Exponential Density and the Normal Distribution?

The Double Exponential Density is similar to the Normal Distribution in terms of shape, but it has heavier tails. This means that it assigns a higher probability to extreme values compared to the Normal Distribution. The Normal Distribution is also a symmetrical distribution, while the Double Exponential Density can be asymmetric.

How is the Double Exponential Density used in statistical analysis?

The Double Exponential Density is commonly used in statistical analysis to model continuous variables that have heavy tails, such as stock prices, income, and wind speeds. It is also used in regression analysis and in the estimation of parameters for other distributions.

What are the advantages and disadvantages of using the Double Exponential Density?

One advantage of using the Double Exponential Density is that it can model data with heavy tails, which is common in many real-world scenarios. It also has a simple form, making it easy to work with mathematically. However, it may not be suitable for data that follows a normal distribution, and it requires a large sample size to accurately estimate its parameters.

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