Proving Lim F(x,y) is the Distribution Function for X

In summary, the marginal distribution for multivariable random variables X and Y can be proven to be ##lim_{y\to \infty}F(x,y)## using upper and lower bounds and taking limits.
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mathman
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TL;DR Summary
Marginal distributions for multivariable
Let F(x,y) be the joint distribution for random variables X and Y (not necessarily independent). Is ##lim_{y\to \infty}F(x,y)## the distribution function for X? I believe it is. How to prove it?
 
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mathman said:
Summary: Marginal distributions for multivariable

Let F(x,y) be the joint distribution for random variables X and Y (not necessarily independent). Is ##lim_{y\to \infty}F(x,y)## the distribution function for X? I believe it is. How to prove it?
This is rather indirect, but I felt like answering this with inequalities, using a thread you were on earlier this year.
ref: https://www.physicsforums.com/threads/joint-pdf-and-its-marginals.965416/

Upper Bound
##F_{X,Y}(x,y)\leq F_X(x) ##

- - - -
with ##A## the event ##X \leq x## and ##B## the event ##Y \leq y##

##F_{X,Y}(x,y)##
##= P\big(A \cap B\big) ##
##\leq P\big(A \cap B\big) + P\big(A \cap B^C\big) ##
##= P\big( (A \cap B) \cup (A \cap B^C)\big ) ##
##= P\big( A\big ) ##
## =F_X(x)##

note: the upper bound does not depend on choice of yLower Bound (see link)
##F_X(x) + F_Y(y) - 1 \leq F_{X,Y}(x,y) ##

putting upper and lower bounds together and taking limits, recalling that Y is a bona fide random variable, so you know its CDF tends to one

## F_X(x) ##
##= \big\{ F_X(x) + 1 - 1\big\} ##
##= \lim_{y \to \infty}\big\{ F_X(x) + F_Y(y) - 1\} ##
##\leq \lim_{y \to \infty} F_{X,Y}(x,y) ##
##\leq F_X(x)##

as desired
 
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1. What is a distribution function?

A distribution function, also known as a cumulative distribution function (CDF), is a mathematical function that describes the probability that a random variable takes on a value less than or equal to a given value.

2. How do you prove that a function is a distribution function?

To prove that a function is a distribution function, you must show that it satisfies three properties: non-decreasing, right-continuous, and limits to 0 and 1 at negative and positive infinity, respectively.

3. What is the importance of proving that a function is a distribution function?

Proving that a function is a distribution function is important because it ensures that the function accurately represents the probability distribution of a random variable. This is essential in statistical analysis and decision-making.

4. Can a function have more than one distribution function?

No, a function can only have one distribution function. This is because a distribution function is uniquely determined by its corresponding probability distribution.

5. Are there any common mistakes when proving a function is a distribution function?

Yes, some common mistakes include not checking all three properties of a distribution function, not considering the domain of the function, and not properly defining the function. It is important to be thorough and precise when proving a function is a distribution function.

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