PDF and CDF Integration Simplification

In summary, the conversation discusses an equation involving two random variables, their PDF and CDF. The question is whether the equation can be written in a specific form. Through factoring and manipulation of the integrand, it is possible to rewrite the equation in the desired form of 1-\int_0^{\infty}F_Y(a)\,f_X(a+\gamma)\,da. However, the solution requires some knowledge of probability.
  • #1
EngWiPy
1,368
61
Hello,
I have this equation:

[tex]\int_{-\infty}^{\gamma}f_X(x)\,dx+\int_{\gamma}^{\infty}F_Y(x-\gamma)\,f_X(x)\,dx[/tex]

where [tex]f_X(x)[/tex] and [tex]F_Y(y)[/tex] are the PDF and CDF of the randome variables X and Y, respectively.

Now the question is: can I write the above equation in the form:

[tex]1-\int_{0}^{\infty}(...)[/tex]

Regards
 
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  • #2
Try starting with

[tex]
\int_{-\infty}^\gamma f_X(x) \, dx = \int_{-\infy}^\infty f_X (x) \, dx - \infty_\gamma^\infty f_X(x) \, dx = 1 - \infty_\gamma^\infty f_X(x) \, dx
[/tex]
 
  • #3
What statdad meant to say was: Try starting with

[tex]\int_{-\infty}^{\gamma} f_X(x)\,dx =
\int_{-\infty}^{\infty} f_X(x)\,dx \;- \,\int_{\gamma}^{\infty}f_X(x\,)dx =
1 \,- \int_{\gamma}^{\infty}f_X(x)dx[/tex]
 
  • #4
D H said:
What statdad meant to say was: Try starting with

[tex]\int_{-\infty}^{\gamma} f_X(x)\,dx =
\int_{-\infty}^{\infty} f_X(x)\,dx \;- \,\int_{\gamma}^{\infty}f_X(x\,)dx =
1 \,- \int_{\gamma}^{\infty}f_X(x)dx[/tex]

Yes indeed, but statdad, in his advanced age, was interrupted by some annoying folks at the door and neglected to fix his post. Thanks.
 
  • #5
D H said:
What statdad meant to say was: Try starting with

[tex]\int_{-\infty}^{\gamma} f_X(x)\,dx =
\int_{-\infty}^{\infty} f_X(x)\,dx \;- \,\int_{\gamma}^{\infty}f_X(x\,)dx =
1 \,- \int_{\gamma}^{\infty}f_X(x)dx[/tex]

Yes, but I want the whole right side be one minus single integral. Is this still doable in some how?
 
  • #6
Yes: you can write

[tex]
1 - \int_\gamma^\infty \left(f_X(x) - F_Y(x-\gamma)f_X(x)\right) \, dx
[/tex]

You should be able to take this and write it as

[tex]
1 - \int_0^\infty ( \cdots ) \, dx
[/tex]

just play around with the integrand.
 
  • #7
statdad said:
Yes: you can write

[tex]
1 - \int_\gamma^\infty \left(f_X(x) - F_Y(x-\gamma)f_X(x)\right) \, dx
[/tex]

You should be able to take this and write it as

[tex]
1 - \int_0^\infty ( \cdots ) \, dx
[/tex]

just play around with the integrand.

Thank you, but this form is not the one in my mind. I need, if possible, in some how, to eliminate the first term, so that the equation looks like:

[tex]1-\int_0^{\infty}F_Y(a)\,f_X(a+\gamma)\,da[/tex]​

Regards
 
  • #8
Look at the integrand in

[tex]
\int_\gamma^\infty \left(f_X(x) - F_Y(x-\gamma)f_X(x)\right) \, dx
[/tex]

You should see a very simple way to factor it and then rewrite it in a form more suitable to your desires for this problem.

Try it - do some work - then post again.
 
  • #9
statdad said:
Look at the integrand in

[tex]
\int_\gamma^\infty \left(f_X(x) - F_Y(x-\gamma)f_X(x)\right) \, dx
[/tex]

You should see a very simple way to factor it and then rewrite it in a form more suitable to your desires for this problem.

Try it - do some work - then post again.

I can't see anything that I can do. :shy:
 
  • #10
Look a little harder. I won't give away the answer.
 
  • #11
statdad said:
Look a little harder. I won't give away the answer.

Just give me a hint, I am not strong in probability.
 
  • #12
saeddawoud said:
Just give me a hint, I am not strong in probability.

Work with the integrand.
 
  • #13
statdad said:
Work with the integrand.

Are you sure that, we can write [tex]\int_\gamma^\infty \left(f_X(x) - F_Y(x-\gamma)f_X(x)\right) \, dx[/tex] as [tex]\int_0^{\infty}F_Y(a)\,f_X(a+\gamma)\,da[/tex]?
 

1. What is the difference between a PDF and CDF?

A probability density function (PDF) represents the relative likelihood of a continuous random variable taking on a particular value. A cumulative distribution function (CDF) represents the probability that a random variable will fall below a certain value. In other words, a PDF shows the distribution of a variable's values, while a CDF shows the probability of those values occurring.

2. How can I manipulate a PDF or CDF?

PDF and CDF manipulation can be done through mathematical operations, such as scaling, shifting, and combining functions. These manipulations can alter the shape and parameters of the distribution, allowing for more accurate modeling of data or prediction of future outcomes.

3. What are the benefits of manipulating a PDF or CDF?

By manipulating a PDF or CDF, scientists and statisticians can better understand the behavior and characteristics of a particular variable or data set. This can lead to more accurate predictions and insights, as well as the ability to compare and contrast different distributions to identify patterns and relationships.

4. Are there any limitations to PDF and CDF manipulation?

While PDF and CDF manipulation can be a powerful tool for data analysis, there are some limitations to consider. Manipulating a distribution too much can lead to overfitting, where the model becomes too specific to the data and cannot be applied to new data. It is important to carefully consider the purpose and goal of the manipulation to avoid this issue.

5. Can PDF and CDF manipulation be used for any type of data?

PDF and CDF manipulation can be applied to any continuous random variable, regardless of the type of data. This includes data from various fields such as physics, finance, and social sciences. However, it is important to ensure that the data is appropriate for the chosen manipulation and that the results are interpreted correctly within the context of the data.

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