Maths - Solving for the CDF of Y=\sqrt{X}

In summary, the given exponential distribution can be used to calculate the cumulative distribution function of Y=\sqrt{X}, which is given by F_Y(y)=1-e^{-y^2/2}. To show this, one can use the integral formula and plug in the relevant equations, or proceed from first principles by calculating P(Y<x) and using the exponential CDF. Either method will arrive at the desired result.
  • #1
Mentallic
Homework Helper
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Homework Statement


If we have the exponential distribution [tex]f_X(x)=\frac{1}{2}e^{-x/2}[/tex] then show that the cumulative distribution function of [tex]Y=\sqrt{X}[/tex] is given by [tex]F_Y(y)=1-e^{-y^2/2}[/tex]

Homework Equations


[tex]F_Y(y)=f_X(x)\cdot\left| \frac{dx}{dy}\right|[/tex]

[tex]F_Y(y)=f_X(h^{-1}(y))\cdot\left| \frac{d(h^{-1}(y))}{dy}\right|[/tex]

The Attempt at a Solution


[tex]Y=\sqrt{X}=h(x)[/tex]

[tex]\therefore h^{-1}(y)=y^2[/tex]

[tex]\frac{d(h^{-1}(y))}{dy}=2y[/tex]

[tex]f_X(h^{-1}(y))=\frac{1}{2}e^{-y^2/2}[/tex]

[itex]\therefore[/itex] after plugging these values into the formula in the relevant equations,

[tex]F_Y(y)=y\cdot e^{-y^2/2}[/tex]

Which is not what I was meant to show. I only had one example in my textbook to go off of and I (from what I can tell) think I applied it correctly to my question, but clearly I haven't. Can someone please guide me in the right direction, and also if you can see anything in my steps that need to be scrutinized, don't be afraid to speak out.
 
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  • #2
You used an incorrect equation.

[itex]f_Y(y)=f_X(x)\cdot\left| \frac{dx}{dy}\right|[/itex].

What you found was the PDF not the CDF. You have to integrate.
 
  • #3
Oh, cheers :biggrin:
 
  • #4
Mentallic said:

Homework Statement


If we have the exponential distribution [tex]f_X(x)=\frac{1}{2}e^{-x/2}[/tex] then show that the cumulative distribution function of [tex]Y=\sqrt{X}[/tex] is given by [tex]F_Y(y)=1-e^{-y^2/2}[/tex]

Homework Equations


[tex]F_Y(y)=f_X(x)\cdot\left| \frac{dx}{dy}\right|[/tex]

[tex]F_Y(y)=f_X(h^{-1}(y))\cdot\left| \frac{d(h^{-1}(y))}{dy}\right|[/tex]

The Attempt at a Solution


[tex]Y=\sqrt{X}=h(x)[/tex]

[tex]\therefore h^{-1}(y)=y^2[/tex]

[tex]\frac{d(h^{-1}(y))}{dy}=2y[/tex]

[tex]f_X(h^{-1}(y))=\frac{1}{2}e^{-y^2/2}[/tex]

[itex]\therefore[/itex] after plugging these values into the formula in the relevant equations,

[tex]F_Y(y)=y\cdot e^{-y^2/2}[/tex]

Which is not what I was meant to show. I only had one example in my textbook to go off of and I (from what I can tell) think I applied it correctly to my question, but clearly I haven't. Can someone please guide me in the right direction, and also if you can see anything in my steps that need to be scrutinized, don't be afraid to speak out.

Rather than plugging in formulas, I think it is better to proceed from first principles: [tex] P\{Y \leq y\} = P\{ \sqrt{X} \leq y \} = P\{ X \leq y^2 \} = \left. (1 - e^{-x/2})\right|_{x=y^2}. [/tex]

RGV
 
Last edited:
  • #5
Ray Vickson said:
P\{ X \leq y^2 \} = \left. (1 - e^{-x/2})\right|_{x=y^2}. [/tex]

Doesn't the jump between these two steps defeat the purpose? I believe I'm meant to use the integral to show that.

Now, if I were to calculate P(Y<x) for example, wouldn't I need to integrate again? Which means that I'd need to find a numerical solution.

edit: Never mind, I wouldn't integrate again.
 
Last edited:
  • #6
Mentallic said:
Doesn't the jump between these two steps defeat the purpose? I believe I'm meant to use the integral to show that.

Now, if I were to calculate P(Y<x) for example, wouldn't I need to integrate again? Which means that I'd need to find a numerical solution.

edit: Never mind, I wouldn't integrate again.

There is no such a thing as "meant to" (unless stated explicitly). Your are "meant to" do anything that is correct and that you feel comfortable doing; aside from that, there are no rules. If you prefer to use the formulas you wrote before, go ahead and do that, but there are other ways to proceed, too, and I showed you one of them (which happens to be the one I personally prefer).

RGV
 
  • #7
Ray Vickson said:
There is no such a thing as "meant to" (unless stated explicitly). Your are "meant to" do anything that is correct and that you feel comfortable doing; aside from that, there are no rules. If you prefer to use the formulas you wrote before, go ahead and do that, but there are other ways to proceed, too, and I showed you one of them (which happens to be the one I personally prefer).

RGV

Sorry, I guess I wasn't clear. What I was trying to say was, didn't you arrive at that answer by integrating? It seems that in between those two steps there was some integration involved. And yes, I've also done essentially what you have there.
 
  • #8
Mentallic said:
Sorry, I guess I wasn't clear. What I was trying to say was, didn't you arrive at that answer by integrating? It seems that in between those two steps there was some integration involved. And yes, I've also done essentially what you have there.

It all depends on where you start. I am so used to the exponential CDF, F(x) = 1 - exp(-ax), that I write it down almost without thinking. Of course it was obtained way in the past by integration. Still, doing its integral is a little bit easier than integrating your f_Y(y).

RGV
 

Related to Maths - Solving for the CDF of Y=\sqrt{X}

1) What is an Exponential Distribution?

An Exponential Distribution is a probability distribution that describes the time between events in a Poisson process. It is often used to model the arrival time of events, such as customer arrivals at a store or the time between phone calls in a call center.

2) How is the Exponential Distribution different from other probability distributions?

The Exponential Distribution is unique because it is a continuous distribution, meaning that the possible values it can take on are not limited to whole numbers. It also has a special property called memorylessness, which means that the probability of an event occurring in the next time interval is not affected by how much time has already passed.

3) What is the formula for the Exponential Distribution?

The formula for the Exponential Distribution is f(x) = λe-λx, where λ is the rate parameter and x is the variable representing time. This formula gives the probability of an event occurring in a specific time interval.

4) What are some real-world applications of the Exponential Distribution?

The Exponential Distribution has many applications in fields such as engineering, finance, and healthcare. Some examples include modeling the time between equipment failures, the waiting time for a loan approval, and the time between patient arrivals in a hospital emergency room.

5) How is the Exponential Distribution related to the Poisson Distribution?

The Poisson Distribution is often used to model the number of events occurring in a specific time interval, while the Exponential Distribution is used to model the time between events. These two distributions are closely related, as the time between events in a Poisson process follows an Exponential Distribution, and the number of events in a given time period follows a Poisson Distribution.

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