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

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Homework Help Overview

The discussion revolves around finding the cumulative distribution function (CDF) of the random variable Y, defined as Y = √X, where X follows an exponential distribution with a specified probability density function (PDF). Participants are exploring the relationship between the CDF of Y and the PDF of X, as well as the necessary transformations and integrations involved in deriving the CDF.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning, Problem interpretation

Approaches and Questions Raised

  • Participants discuss the application of transformation techniques to derive the CDF from the PDF. There are questions regarding the correctness of the equations used and whether integration is necessary to arrive at the CDF. Some participants express uncertainty about the steps taken and seek clarification on the integration process.

Discussion Status

There is an ongoing exploration of different methods to approach the problem, with some participants suggesting alternative perspectives on deriving the CDF. While some guidance has been offered regarding the use of integration and the interpretation of the problem, there is no explicit consensus on the best approach yet.

Contextual Notes

Participants note the potential for confusion regarding the transition between the PDF and CDF, as well as the necessity of integrating to find the CDF. There is also mention of the original poster's reliance on a single textbook example, which may limit their understanding of the broader context.

Mentallic
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Homework Statement


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

Homework Equations


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

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

The Attempt at a Solution


Y=\sqrt{X}=h(x)

\therefore h^{-1}(y)=y^2

\frac{d(h^{-1}(y))}{dy}=2y

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

\therefore after plugging these values into the formula in the relevant equations,

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

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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You used an incorrect equation.

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

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

Homework Statement


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

Homework Equations


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

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

The Attempt at a Solution


Y=\sqrt{X}=h(x)

\therefore h^{-1}(y)=y^2

\frac{d(h^{-1}(y))}{dy}=2y

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

\therefore after plugging these values into the formula in the relevant equations,

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

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: P\{Y \leq y\} = P\{ \sqrt{X} \leq y \} = P\{ X \leq y^2 \} = \left. (1 - e^{-x/2})\right|_{x=y^2}.

RGV
 
Last edited:
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:
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
 
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.
 
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
 

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