Statistics Problem: finding a PDF using the CDF technique

In summary, the conversation discusses using the CDF technique to find the pdf of T=X+Y, with X and Y being continuous random variables with a given joint pdf. The conversation also addresses the need for different integrals for t > 1 and t < 1, and suggests using the cdf method despite its complexity. The conversation ends with the individual realizing their mistake and thanking the others for their help.
  • #1
_Steve_
19
0
Hey guys, I'm stuck on a question in my homework assignment and I was wondering if you could push me in the right direction? So here's the question:

X and Y are continuous random variables with joint pdf f(x,y)= 4xy (0<x<1, 0<y<1, and otherwise 0). Find the pdf of T=X+Y using the CDF technique.

So this is how I started off, I first let G(t) be the CDF of T, then I look at three different cases:
t<=0: G(t) = P[X+Y<=t] = 0
t>=2: G(t) = P[X+Y<=t] = 1
0<t<2: G(t) = P[X+Y<=t] = ?
So here I'm a little confused, I'm trying to figure out the limits of the double integral I'm supposed to take, I think I might have to take one integral from 0<t<1, then another from 1<=t<2, but then I'm stuck with two "functions" (one from (0,1), one from [1,2)) for my g(t). Are there any possible limits for this double integral that would save me from having to separate 0<t<2 into two double integrals?
 
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  • #2
_Steve_ said:
Hey guys, I'm stuck on a question in my homework assignment and I was wondering if you could push me in the right direction? So here's the question:

X and Y are continuous random variables with joint pdf f(x,y)= 4xy (0<x<1, 0<y<1, and otherwise 0). Find the pdf of T=X+Y using the CDF technique.

So this is how I started off, I first let G(t) be the CDF of T, then I look at three different cases:
t<=0: G(t) = P[X+Y<=t] = 0
t>=2: G(t) = P[X+Y<=t] = 1
0<t<2: G(t) = P[X+Y<=t] = ?
So here I'm a little confused, I'm trying to figure out the limits of the double integral I'm supposed to take, I think I might have to take one integral from 0<t<1, then another from 1<=t<2, but then I'm stuck with two "functions" (one from (0,1), one from [1,2)) for my g(t). Are there any possible limits for this double integral that would save me from having to separate 0<t<2 into two double integrals?

You are correct in that you do need different integrals for t > 1 and t < 1. If you use the cdf method this is unavoidable. (Other, completely different, methods are easier, but you are told not to use them.)

RGV
 
  • #3
Ray Vickson said:
You are correct in that you do need different integrals for t > 1 and t < 1. If you use the cdf method this is unavoidable. (Other, completely different, methods are easier, but you are told not to use them.)

RGV

Oh, okay, so how do I get a g(t) with only one function from 0<t<2 instead of having:
g(t)={f(t), t<1
h(t), t>1)
?

Does it make sense to write:
G(t)={0, t<0
F(t), t<1
H(t), t>1
1, t>2}
as:
G(t)={0, t<0
F(t)+H(t), 0<t<2
1, t>2}
?

EDIT: Nevermind, I think I was doing the other part of the question wrong.. thanks for your help!
 
Last edited:

FAQ: Statistics Problem: finding a PDF using the CDF technique

What is a PDF?

A PDF (Probability Density Function) is a mathematical function that describes the relative likelihood of a random variable taking on a given value. It is used to represent the distribution of a continuous random variable.

How is a PDF related to a CDF?

A CDF (Cumulative Distribution Function) is the integral of a PDF. It represents the probability that a random variable will take on a value less than or equal to a given value. In other words, the CDF is the cumulative sum of the probabilities of all values up to a specific point on the PDF curve.

How do you find a PDF using the CDF technique?

To find a PDF using the CDF technique, you first need to have the CDF equation. Then, you can differentiate the CDF equation to obtain the PDF equation. This process is known as taking the derivative. Once the PDF equation is obtained, you can use it to calculate the probability at any given point on the PDF curve.

What is the significance of the PDF in statistics?

The PDF is an important concept in statistics because it allows us to understand the distribution of continuous random variables. By analyzing the shape of the PDF curve, we can make inferences about the likelihood of different values occurring and the overall behavior of the random variable.

What are some common applications of the CDF technique?

The CDF technique is commonly used in various fields, including finance, economics, and engineering. It is used to model and analyze data related to continuous random variables, such as stock prices, economic variables, and physical measurements. Additionally, the CDF technique is used in hypothesis testing and probability calculations in statistical analysis.

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