Cumulative distribution function

In summary, the conversation discusses determining the joint cumulative distribution function F(x,y) of two continuous random variables X and Y, as well as obtaining the marginal cumulative distribution functions F_X (x) and F_Y (y). The process involves solving integrals for each of the three given cases and using a specific formula for F_X (x) and F_Y (y).
  • #1
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Homework Statement



Let X and Y be continuous random variables having joint probability density function

[tex]f(x,y) = e^{-y}[/tex] if 0 [tex]\leq x \leq y[/tex]

A) Determine the joint cumulative distribution function F(x,y) of X and Y. (Hint: Consider the three cases 1) [tex]x \leq 0[/tex] or [tex]y \leq 0[/tex] 2) 0 < x < y 3) 0 <y < x

B) Let [tex]F_X (x)[/tex] and [tex]F_Y (y)[/tex] be the marginal cumulative distribution functions of X and Y. One can show that [tex]F_X (x) = Lim_{Y \rightarrow \infty} F(x,y)[/tex] and [tex]F_Y (y) = Lim_{X \rightarrow \infty} F(x,y)[/tex]. Use this result to obtain [tex]F_X (x)[/tex] and [tex]F_Y (y)[/tex]

Homework Equations





The Attempt at a Solution



Not sure how to start with A).

I know that [tex] F(x,y) = \int^x_{- \infty} \int^y_{- \infty} f(x,y) dy dx[/tex]

Does it mean for the case where x < 0 it would be:

[tex] F(x,y) = \int^0_{- \infty} \int^y_{- \infty} f(x,y) dy dx[/tex] ?
 
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  • #2
So considering the 3 cases, would it be the following series of integrals:

[tex]\int^x_{- \infty}\int^0_{- \infty}f(u,v) dv du + \int^x_{- \infty}\int^y_{x}f(u,v) dv du + \int^x_{- \infty}\int^x_{y}f(u,v) dv du[/tex]
 

1. What is a cumulative distribution function (CDF)?

A cumulative distribution function (CDF) is a statistical function that represents the cumulative probability that a random variable takes on a certain value or falls within a certain range. It is used to describe the distribution of a random variable and is often displayed graphically as a curve.

2. How does a CDF differ from a probability density function (PDF)?

A CDF is the integral of a PDF, which means it shows the probability of a random variable being less than or equal to a certain value. In contrast, a PDF shows the probability of a random variable taking on a specific value. While a PDF can take on values greater than 1, a CDF always ranges from 0 to 1.

3. What is the relationship between a CDF and a percentile?

A CDF can be used to determine the percentile of a given value in a distribution. For example, if the CDF of a random variable at a certain value is 0.75, it means that 75% of the data falls below that value, making it the 75th percentile.

4. How is a CDF used in hypothesis testing?

In hypothesis testing, a CDF can be used to determine the p-value, which represents the probability of obtaining a test statistic at least as extreme as the one observed, assuming the null hypothesis is true. The p-value is compared to a significance level to determine the statistical significance of the results.

5. Can a CDF be used for discrete and continuous random variables?

Yes, a CDF can be used for both discrete and continuous random variables. For discrete variables, the CDF is a step function, while for continuous variables, it is a smooth curve. However, in both cases, the CDF represents the cumulative probability of the random variable taking on a certain value or falling within a certain range.

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